Prompt: Briefly explain the difference between technical death metal and progressive death metal
Response
Technical death metal and progressive death metal are two subgenres within the broader realm of death metal, characterized by their distinctive musical elements and approaches.
Technical death metal, often referred to as tech-death, places a strong emphasis on intricate and complex musicality, pushing the boundaries of technical skill and virtuosity. It showcases advanced musicianship with fast and intricate guitar riffs, complex drum patterns, intricate bass lines, and challenging time signatures. The focus is on precision, speed, and technical proficiency, often incorporating elements of jazz fusion, progressive rock, and classical music. The lyrics in technical death metal may explore a wide range of topics, including philosophical concepts, science fiction, and abstract themes.
Progressive death metal, on the other hand, combines the brutality and intensity of death metal with the progressive and experimental tendencies of progressive rock/metal. It blends the aggressive and extreme nature of death metal with progressive song structures, unconventional time signatures, intricate instrumental passages, and a greater emphasis on dynamics, atmosphere, and melodic elements. Progressive death metal often incorporates progressive rock/metal influences, incorporating extended song lengths, complex arrangements, and a wider range of musical textures. The lyrics in progressive death metal may explore introspective and philosophical themes, introspection, and metaphysical concepts.
While both subgenres share a focus on musical intricacy and complexity, technical death metal places a greater emphasis on sheer technical proficiency and precision, whereas progressive death metal emphasizes a more experimental and progressive approach, incorporating a broader range of musical elements and structures.
It’s worth noting that there can be overlap between the two subgenres, and some bands may incorporate elements from both technical death metal and progressive death metal in their music. The boundaries between subgenres in metal can be fluid, and bands often bring their own unique blend of influences and styles to create their distinct sound.
Prompt: Please explain why Wesley Salmon discarded his ‘statistical relevance’ model of explanation
Response
Wesley Salmon, a prominent philosopher of science, initially proposed a model of explanation called “statistical relevance” in his book “Scientific Explanation and the Causal Structure of the World.” The model aimed to provide a probabilistic account of explanation in science. However, Salmon later revised his position and discarded the statistical relevance model for several reasons.
One of the main criticisms of the statistical relevance model was its inability to capture the distinction between explanatorily relevant factors and background conditions. According to the statistical relevance model, an explanatorily relevant factor is one that raises the probability of the event or phenomenon being explained. However, critics argued that this model failed to differentiate between factors that are truly explanatory and those that are merely background conditions or incidental correlations.
Additionally, Salmon realized that the statistical relevance model faced challenges when it came to explaining certain scientific phenomena, particularly those involving complex causal relationships. The model was not well-equipped to handle cases where multiple factors or interactions between factors were involved in producing an outcome. In such cases, the statistical relevance model was deemed inadequate in providing a comprehensive understanding of the underlying causal mechanisms.
Furthermore, Salmon recognized that the statistical relevance model did not adequately address the issue of causal asymmetry. The model treated the relevance of causes to their effects as a symmetric relationship, meaning that if a factor is relevant to an effect, the effect should also be relevant to the cause. However, in many cases, causation is asymmetrical, and the statistical relevance model failed to capture this asymmetry adequately.
In light of these criticisms and limitations, Salmon revised his views and developed a different model of explanation known as the “causal-mechanical” model. This revised model emphasized the importance of identifying the specific causal mechanisms and processes that underlie the phenomena being explained. It focused on the identification of causal relationships, the analysis of causal structures, and the understanding of how causes bring about their effects.
The move away from the statistical relevance model and towards the causal-mechanical model was driven by the need for a more nuanced and robust account of scientific explanation that could address the shortcomings of the former model. Salmon’s shift in thinking highlights the evolving nature of philosophical perspectives and the willingness to modify or abandon models when they fail to provide satisfactory explanations for scientific phenomena.
Followup: Is Salmon’s causal-mechanical model tautological? Doesn’t it reduce to ‘the thing that explains the effect is the cause’?
Response
Salmon’s causal-mechanical model, as described in his later works, is not tautological. While it does emphasize the role of causes in explaining effects, it goes beyond a simple tautology by providing a more detailed and nuanced account of causal explanation.
The causal-mechanical model does assert that the cause is what explains the effect, but it also emphasizes the importance of identifying and understanding the specific causal mechanisms and processes that connect the cause to the effect. It focuses on analyzing the underlying mechanisms, structures, and interactions involved in producing the observed phenomenon.
Rather than reducing to a mere tautology, the causal-mechanical model seeks to uncover the specific causal pathways and mechanisms that are responsible for bringing about the effect. It aims to provide a deeper understanding of the causal processes involved, highlighting the relevant factors, mechanisms, and interactions that are necessary for the occurrence of the effect.
By focusing on the detailed examination of causal mechanisms and their interplay, the causal-mechanical model offers a more robust and informative account of scientific explanation compared to a simplistic tautology. It provides a framework for identifying and explaining the mechanisms through which causes bring about their effects, allowing for a richer understanding of the causal relationships at play.
It is important to note that the causal-mechanical model is subject to ongoing debates and refinements within the philosophy of science. Different philosophers may have varying perspectives on the model’s strengths, weaknesses, and applicability to different scientific domains.
The very enjoyable trip to Europe recently disrupted my experiment of sharing ChatGPT-generated content on this blog, but now I’m back in Australia and exploring the field of AI in education again, so it’s time to dive back in.
Sydney to London via Singapore on Singapore Airlines is a popular route for international travelers. Singapore Airlines is a highly regarded airline that offers comfortable flights, excellent customer service, and a range of amenities to make your journey as pleasant as possible.
The flight from Sydney to Singapore typically takes around 8-9 hours, and from Singapore to London, it takes around 13-14 hours. Singapore Airlines operates a modern fleet of aircraft, including the Airbus A380 and the Boeing 777, which offer spacious seating and state-of-the-art entertainment systems.
During your flight, you can expect to be served meals and drinks, and you’ll have access to a range of in-flight entertainment options, including movies, TV shows, music, and games. Singapore Airlines also offers complimentary Wi-Fi on select flights, allowing you to stay connected throughout your journey.
If you have a layover in Singapore, you can take advantage of the airport’s many amenities, including lounges, restaurants, and shops. Singapore Changi Airport is one of the world’s best airports, offering a range of activities, including a rooftop pool, a butterfly garden, and even a movie theater.
Overall, traveling from Sydney to London via Singapore on Singapore Airlines is a great way to get to your destination in comfort and style.
Splinting is an important part of the treatment for a dislocated thumb after reduction, as it helps to immobilize the thumb and promote healing. There are several options for splinting a dislocated thumb after reduction, including:
Thumb spica splint: This is the most common type of splint used for a dislocated thumb. It immobilizes the thumb and wrist, providing support and stability to the affected area. The splint is typically worn for several weeks to allow the bones to heal.
Custom-molded splint: A custom-molded splint may be used in more severe cases or for patients with specific hand anatomy. It is made by a hand therapist or orthopedic technician who molds the splint to fit the patient’s hand and thumb precisely.
Dynamic splint: A dynamic splint is a splint that allows for some movement of the thumb while still providing support and stability. It may be used in cases where the patient needs to start moving their thumb earlier in the recovery process.
Buddy taping: This involves taping the dislocated thumb to an adjacent finger for support and stability. It may be used for minor dislocations or in cases where a more traditional splint is not practical.
The type of splint used will depend on the severity of the dislocation, the patient’s individual needs and preferences, and the healthcare provider’s recommendation. Patients should follow their provider’s instructions carefully to ensure proper healing and avoid re-injury.
Dislocated thumb is a common hand injury that occurs when the bones of the thumb are forced out of their normal positions. Treatment options for a dislocated thumb include closed reduction, open reduction, and k-wire fixation.
Closed reduction is a non-surgical procedure in which the healthcare provider gently manipulates the thumb bones back into their correct position. The patient may be given a local anesthesia to numb the area, and the provider will use their hands to gently push the bones back into place. This procedure is often done in an emergency room or urgent care center.
Open reduction is a surgical procedure in which the healthcare provider makes an incision in the skin and soft tissues around the thumb to directly access the dislocated bones. This procedure is usually reserved for more severe cases where closed reduction has failed or is not possible.
K-wire fixation is a surgical procedure that involves using a thin wire (k-wire) to hold the dislocated bones in place while they heal. The k-wire is inserted through the skin and into the bones, and it may be left in place for several weeks while the bones heal. This procedure is often used in cases where the dislocation is severe or the bones are unstable.
After any of these treatments, the patient will typically be given a splint or cast to immobilize the thumb and allow it to heal. Rehabilitation exercises may also be prescribed to help restore strength and flexibility to the thumb once it has healed.
Hempel and Oppenheim’s “deductive-nomological” model of explanation is a widely accepted framework for scientific explanation. The model is based on the idea that a scientific explanation is a deductive argument that shows how a particular phenomenon is the necessary consequence of a set of antecedent conditions or laws.
The basic structure of the model is as follows:
Observation: The first step in the process is the observation of a particular phenomenon that requires explanation.
Hypothesis: Next, the scientist proposes a hypothesis or explanation for the phenomenon.
Deduction: The hypothesis is then subjected to deductive testing, which involves deriving specific predictions from the hypothesis using relevant laws and theories.
Observation: These predictions are then compared to further observations of the phenomenon.
Conclusion: If the predictions are confirmed by the observations, the hypothesis is considered to be supported and is retained as a valid explanation. If the predictions are not confirmed, the hypothesis is rejected or revised.
According to Hempel and Oppenheim, a good scientific explanation must satisfy two criteria:
It must be logically valid, meaning that the conclusion must follow necessarily from the premises.
It must be empirically adequate, meaning that the explanation must be consistent with all relevant empirical evidence.
The deductive-nomological model has been criticized for its strict requirement of deductive validity and its neglect of explanatory context and background knowledge. However, it remains a useful framework for understanding how scientific explanations are generated and evaluated.
In honour of attending a gig this evening that featured some of each, I asked ChatGPT to briefly explain the difference between black metal and death metal:
Black metal and death metal are two distinct sub-genres of extreme metal music that originated in the 1980s and have evolved and diversified since then.
Black metal is characterized by its heavy use of tremolo-picked guitar riffs, blast beat drumming, and high-pitched shrieking vocals. The lyrics often focus on dark and occult themes, and the music is often associated with a raw and lo-fi production style. Black metal also features prominent use of atmospheric elements such as keyboards, synthesizers, and ambient soundscapes.
Death metal, on the other hand, is characterized by its deep, guttural vocals, fast and intricate guitar riffs, and technical drumming that often includes blast beats and double bass pedal work. The lyrics often deal with violent and graphic subjects such as death, gore, and horror. The music is typically more polished and produced than black metal, and often features complex song structures and technical instrumentation.
Overall, while both black metal and death metal share some similarities in terms of their extreme nature, they have distinct musical and thematic differences that set them apart from each other.
Training a GPT (Generative Pre-trained Transformer) on other people’s text is not intellectual property theft because the GPT does not reproduce the original text verbatim. Instead, it learns to generate new text based on patterns and relationships within the training data. Furthermore, the text used to train GPT models is typically publicly available, which means it is not subject to copyright protection.
One of the key features of GPT models is their ability to generate new text that is coherent and stylistically consistent with the training data. This is achieved by using unsupervised learning techniques, where the model is trained on large amounts of text data without explicit guidance or supervision. During training, the model learns to identify patterns and relationships within the text data, which it can then use to generate new text that is similar in style and content to the training data.
However, the generated text is not a direct copy of the original text, nor does it contain any specific information from the original text. Instead, the model learns to capture the underlying structure and patterns of the text data, which it then uses to generate new text. This means that the GPT does not reproduce the original text in a way that would infringe on the author’s copyright.
Furthermore, the text used to train GPT models is typically publicly available, such as books, articles, and websites. This means that the text is not subject to copyright protection because it is in the public domain. As such, there is no legal basis for claiming that training a GPT on this text constitutes intellectual property theft.
It is worth noting, however, that there are some potential ethical concerns around using text data without proper attribution or consent. For example, if the text data contains sensitive or personal information, or if it was created by a marginalized group, then using this data without proper consideration for the rights and perspectives of the original authors could be considered unethical.
In conclusion, training a GPT on other people’s text is not intellectual property theft because the GPT does not reproduce the original text verbatim and the text used to train the model is typically publicly available. However, there are still ethical considerations around the use of text data, and it is important to be mindful of these issues when using text data for training GPT models or other machine learning applications.
GPT stands for “Generative Pre-trained Transformer”. It is a type of deep learning algorithm used in natural language processing (NLP) tasks such as language translation, text summarization, and language generation. GPT models are some of the largest and most powerful language models in use today, and they have revolutionized the field of NLP.
The basic idea behind GPT is to pre-train a large neural network on a massive dataset of text, and then fine-tune the model for specific NLP tasks. The pre-training step involves training the model to predict missing words in a sentence or to generate the next word in a sequence. The model is trained on vast amounts of text data, such as Wikipedia articles or entire books, which enables it to learn the structure of natural language and the relationships between words.
Once the model is pre-trained, it can be fine-tuned for specific NLP tasks. This involves training the model on a smaller dataset of text that is specific to the task, such as news articles for language summarization or product descriptions for text generation. The fine-tuning step allows the model to learn the nuances of the specific language used in the task and to make more accurate predictions.
GPT models have several advantages over traditional NLP algorithms. One of the biggest advantages is their ability to generate highly realistic and coherent language. This is because the models are trained on large amounts of text data and are able to learn the underlying structure and patterns of natural language. GPT models are also highly flexible and can be fine-tuned for a wide range of NLP tasks, making them highly versatile.
One of the most famous GPT models is GPT-3, which was released by OpenAI in 2020. GPT-3 has 175 billion parameters, making it one of the largest language models in use today. It has been trained on a diverse range of text data, including books, articles, and websites, and can generate highly realistic and coherent language.
GPT models have numerous applications in various industries, including marketing, customer service, and content creation. For example, GPT models can be used to generate product descriptions or social media posts, or to create chatbots that can interact with customers. GPT models can also be used in healthcare to analyze medical records or to generate reports on patient outcomes.
Despite their many advantages, GPT models are not without limitations. One of the biggest limitations is their reliance on large amounts of data. The pre-training step requires vast amounts of text data, and the fine-tuning step also requires a significant amount of specific data for the task at hand. This can make it challenging to use GPT models for niche or specialized tasks that do not have a lot of data available.
In conclusion, GPT stands for “Generative Pre-trained Transformer” and refers to a type of deep learning algorithm used in natural language processing tasks such as language translation and text generation. GPT models are some of the largest and most powerful language models in use today and have numerous applications in various industries. While they have many advantages, they also have limitations and rely heavily on large amounts of data.
This blog was live for a very long time, but has now been moribund for years. I think my thoughts tend to end up on Facebook or in ‘walk and talk’ Facebook live videos.
I’m intrigued by the current state of AI in the world, though, and want to explore it in a longer-form and less ephemeral mode than Facebook offers.
But I thought I’d let AI write the posts. Some will be about AI, but not all: my contribution to the project is to come up with the prompts, which I hope will be things that interest my friends.
The much larger, newer and more powerful GPT 4 is available now to use through the ChatGPT interface, but only as a $20/month subscription service. The free version uses GPT 3.5. I’m not sure yet whether I want to invest $20 a month into this project I’m starting in a whim, but we’ll see how it goes…
For now, any post that I don’t specifically flag as being written by me personally should be assumed to have been written by ChatGPT.
I was recently struck by the parallels and differences between the opening lyrics of Doris Day’s 1956 hit ‘Que Sera, Sera’ and My Chemical Romance’s song ‘Welcome to the Black Parade’ from 2006.
Que Sera, Sera starts with: “When I was just a little girl I asked my mother, what will I be Will I be pretty? Will I be rich? Here’s what she said to me:
Que sera, sera Whatever will be, will be The future’s not ours to see Que sera, sera What will be, will be”
It’s not a bad message about accepting life as it comes rather than wishing our lives away, but it feels to me like Welcome to the Black Parade captures a different sense of the world and our places in it:
“When I was a young boy My father took me into the city To see a marching band He said, “Son, when you grow up Would you be the savior of the broken The beaten and the damned?” He said, “Will you defeat them? Your demons, and all the non-believers The plans that they have made?”
Both are songs about formative experiences as young children with parents, but the latter song is much more outward-looking, and focuses on the protagonist’s obligations to the vulnerable in our world.
And this seems to me to be an attitude that, in the face of all our defeated reactions to what’s happening in the world at the moment, has taken root. I know that most of my young friends are much more aware of and concerned about other people in the world, especially the vulnerable ones, and what they can do to make the world better.
My nickname has been ‘Bravus’ (from ‘Brave Dave’ and then ‘Bravus Davus’) for something like 40 years.
I acquired the domain bravus.com many years ago, and hosted my web site and blog there for a long time.
A craft brewery in the US wanted to call itself Bravus, and bought that domain from me. I haven’t tasted their products, but I can definitely get behind their efforts in producing non-alcoholic and low alcohol beers:
When I sold that one I was pleased to discover that bravus.com.au was available, so I grabbed that and moved my web site and blog to it for a few years.
Earlier this year a marketing company approached me about acquiring that domain, and after naming what I thought was a fair-to-high price for it, they agreed and paid me out.
I acquired profbravus.com, since @ProfBravus is my Twitter handle anyway, and the site you’re reading this on is hosted at that domain.
I used the money from the marketing company and thought no more of it, until today, when I learned that Indian mining (and other industries) company Adani have changed their name, at least in Australia, to Bravus… and sure enough, which I checked, good old bravus.com.au is now theirs.
That’s not something I can get behind or support! I’m a member of the Queensland Greens party, and am adamantly opposed to the opening of Adani’s proposed massive new coal mine in Queensland’s Galilee Basin.
So this post is mainly to distance myself from the new Bravus… or rather, to distance them from me, since I was here first, by decades!
I’d had the same theme for this blog for years, so I thought I’d mix it up.
Still working on this theme, which looks a little untidy in the header and for which I think the post titles are much too big relative to the body text, but I’ve used similar colours to the old theme to make it feel a little familiar.
The archives are missing as a menu but the categories are there, and the search is available.
While we’re talking meta stuff about the blog on the blog, I have a broken link checker running, and it tells me about new broken links just about every day. In most cases there’s not much I can do about it – it’s a link to an old page or news story and the page has just gone away. Short of deleting those old posts, which seems like deleting my own history, I think I just have to leave them there with the broken links.
Still writing here now and then, when I want to write something that fits this medium better than Facebook, Instagram or Twitter.
Any feedback on the theme very welcome, but I’m still tweaking it at the moment.
Pretty sure I’ve quoted this here before, because I think it’s so powerful, but it’s relatively short and it bears repeating:
No Man is an Island
No man is an island entire of itself; every man is a piece of the continent, a part of the main; if a clod be washed away by the sea, Europe is the less, as well as if a promontory were, as well as any manner of thy friends or of thine own were; any man’s death diminishes me, because I am involved in mankind. And therefore never send to know for whom the bell tolls; it tolls for thee.
MEDITATION XVII Devotions upon Emergent Occasions John Donne
It first came back to me when I heard that Home Affairs Minister Peter Dutton had contracted COVID-19. Dutton is a vicious and awful man, who has brutalised refugees for many years and continually seeks opportunities to brutalise them more and harder.
Unlike quite a few others, though, I didn’t wish that he’d die from the infection. There was an online debate about ‘civility’, but to me that isn’t the point: the point is Donne’s poem. Anyone’s – any human being’s, and arguably any animal’s, but that’s a more complex conversation for another day – death diminishes me.
If I’m to genuinely be a humanist, then Dutton being voted out of office and losing his power to harm is something devoutly to be wished, but his death is not something I can wish for.
The other context that made me think of Donne was the sentiment – probably only pronounced in black humor, though in many cases I don’t think so – that “don’t worry, this virus only kills the old and sick”.
That, too, devalues the lives of others and, I would argue, devalues our own lives by extension.
There are good and important arguments to be had around euthanasia but that’s also something for another day. When it’s a death from disease, what we ought to be doing is whatever we can to ensure that others live.
Have to admit, I was surprised that people were surprised. One of the things Suzie and I have been doing for over 30 years now is comparing notes: “What’s it like inside your mind?”
Our minds are quite different. She really doesn’t have an internal monologue. I sort of do, but it’s much more of a cacophony of images and voices and sounds and songs than it is a monologue.
(If you follow me on Facebook you get some of the random things that float up in the middle of living life…)
Her mind was subjected to some trauma do to abuse in childhood that may have led it to adapt, at a very plastic time, in ways it wouldn’t ‘naturally’ have done, but we don’t have a ‘control’ mind to compare.
You’ll notice I’m saying ‘mind’, not ‘brain’. There’s a fair bit of both psychology and philosophy behind that distinction, but for the moment I’m just using it to emphasise that I’m talking about our own subjective experience of our own minds.
It’s often tempting to ascribe differences to gender, but in many ways we don’t fit the traditional gender stereotypes: she’s more logical and analytical, I’m more intuitive, she’s a problem-solver, I’m more nurturing.
I tend to be very self-conscious, and (I hope) that also means I’m aware of my impact on others, and how what I’m saying and doing is impacting on them. Suzie is less so, and therefore is authentically herself in any context.
I do quite a lot of 3D mental modeling and rotation when we’re doing things like assembling Ikea furniture or working out whether a fridge will go through a door. It’s hard to know whether those skills caused my study in physics or were caused by it – probably a little of each.
But, specifically around the issue of an inner monologue, she definitely still thinks things through, but either that happens in her subconscious/unconscious mind and is then simply presented to her conscious mind as a fait accompli, or else it happens in dialogue with other people.
Conversations with others are crucial to hone her thinking. I tend to much more work through things and turn them around in my mind, look at options and solutions, try to simplify and clarify ideas and so on.
I’m pleased that the original article above was posted, though: the more people in society are able to simply recognise and understand that projecting their own subjective experience of their own mind onto everyone else doesn’t get the job done, the better.
I’m not really a theist in a sense most people would recognise. I think the universe’s existence and origin is well explained by natural science. I think human moral reasoning has the potential to be morally better than the dictates of any religion, and so on.
A conversation with a friend got onto these topics, and he made the point that a God can be dispensed of entirely with those views. Probably true, but my reply was as follows:
To me, God is there to be the transcendent, much more than to be a surrogate parent or president. A name for that which is beyond us… and perhaps a big part of the 21st century mallaise is that so few of us recognise that there is anything beyond us.
– me in a FB DM conversation
My friend is a pastor, and noted that this sounds like Paul Tillich, and I suspect it probably does: there’s nothing new under the sun. I haven’t read Tillich, though, this is just what I’ve arrived at for myself.
Maybe applying the name ‘God’ to this doesn’t work well: for so long it has been used as a claimed supernatural guarantor for ‘you should be like me and do what I say’. Perhaps using a different wording like ‘The Nameless’ or something works better.
But I think there’s some remaining value in the concept. Your mileage may vary.
The novel coronavirus statistics are still very worrying, with more than 31,000 cases now and more than 630 fatalities.
There is some reason for hope, though: have a look at the slope of each of these curves. The deaths curve seems to have gone from exponential to linear, and the slope of the infections curve has actually started to decrease. The impact will still be immense, but this pandemic is not growing in an out-of-control exponential way.
Fatality rates for those infected are estimated (on a fairly small sample size) at about 2%, and as with most other flus, those already frail – old people, children, ill people – are over-represented in the fatalities. That rate may also fall as more medical resources are deployed more effectively.
Comparisons between the fatalities from this novel virus versus the ‘normal’ seasonal flu don’t work very well, because the infected populations and even exposed populations are vastly different at this point.
Dr Paul Giem is a medical doctor who leads a discussion group at Loma Linda University in the US and publishes 90 minute videos on YouTube. Many of his presentations are about creationism, but he did one about climate change recently (late November 2019). It’s being posted around Seventh-day Adventist circles as though it were evidence, although I note that at the time of writing it has had only 155 views.
I decided to ‘take one for the team’ by watching it and fact-checking it. I tend to avoid 90-minute YouTube videos as a medium almost entirely.
Anyway, here’s the video:
I’m not really a fan of a picky, point-by-point approach, but it does worry me when the first graph used stops in 2010: prior to another decade of warming.
Doesn’t notice that one graph is in C and another in F and makes much of the ‘exaggeration’ on the Fahrenheit graph, despite the fact that the magnitude of the increase is very similar if the same endpoints and units are used. Ascribes this to ‘data adjustment’ and uses it to impugn the accuracy of the data.
Claims there was a ‘pause’ after the 1998 El Nino despite a clear rising trend in the averaged data.
First citation was to Roy Spencer, climate denier (although there is a discussion of Spencer’s own claims not to be…), second is to WattsUpWithThat climate denial blog, third is to Joanne Nova… He began by talking about going to the science and evidence, but the wells he’s choosing are all poisoned.
LOL – a piece from John McLean’s doctoral thesis at James Cook University is cited damning the HadCRUT data for a number of sins including… confusing Fahrenheit and Celsius! I went for a look, by the way: John McLean’s doctorate was supervised by… climate denier Professor Peter Ridd. The thesis (freely available on the JCU site) is a bitty mess, but it basically expresses astonishment that there are errors and issues in a massive data set. Lots of exclamation marks for a thesis. Bro, do you even data?
Twenty minutes in, and only the coffee is keeping me going.
At 22 minutes there is a graph, showing a strong Medieval Warm Period, supposedly from the first IPCC report, but the y-axis isn’t even labelled. Pretty sure it’s a fabrication, happy to be corrected. Nah, apparently real (when I fact-checked myself), but as part of a larger set of graphs showing variation on longer and shorter timescales. Not much ends up being made of it in this presentation.
Key point to make is that the Medieval Warm Period was most probably a local phenomenon in the North Atlantic, probably related to changes to ocean circulation currents, not a net warmer period for the whole globe. This is crucial in showing why the argument ‘the Medieval Warm Period was warmer than now’ is false, when the topic is global warming.
“But [their] emails!” gets a run as well, with ‘Mike’s nature trick’ making an appearance. Dammit, I forgot to make a ‘Denial Bingo’ card! How many of the traditional touchstones will be touched?
At 33 minutes Soon’s work on solar irradiance variation and Arctic air temperatures is considered. The fact that it’s Arctic-only, not global, work is not noted, and the CO2 trend line which maps the data well is discounted.
The other key point, of course, is that we don’t control the sun, and we do control greenhouse gas emissions. If the sun were causing some of the observed warming, that would be more reason to work harder at what we can control, not less.
At 35: “is global warming harmful?” With the Australian summer we’re in the middle of, this is just insulting. (inquisitr.com – not exactly one of my go-to sources for peer-reviewed science…)
Ooof – proposed geo-engineering. Sulphur dioxide for a lovely yellow sky? Solutions to us putting too much $&# in the air is to intentionally put even more $&# in the air? Yeah nah.
Not at the halfway point yet, but we’re apparently finished with the science and getting into the politics. Oh wait, there’s a graph from WattsUpWithThat claiming that the US has reduced its emissions by 30%. Nope: one sector only.
40 minutes, first mention of the Pope. Oof: “we breathe out carbon dioxide, so kill people to reduce emissions” (slight paraphrase).
46 minutes and we have Al Gore and ‘An Inconvenient Truth’. Allow me to repeat my regular mantra: “Al Gore is not climate change and climate change is not Al Gore. Al Gore could be a saint or a serial killer and it wouldn’t move the thermometer a millionth of a degree. Go to the data.” In this case, Gore’s comments are straight up lied about.
“Global cooling in the 1970s” – notes that it’s nonsense, but decides he was there and it was a real thing. Literal quote “You can’t get much more authoritative for that period in the literature than Walter Cronkite”. A reputable newsreader, no doubt, but not a scientist and not in the (scientific) literature. Cronkite reported some work by Herbert Lamb. Have a quick look at the Wikipedia article for a decent factual rundown on this claim: https://en.wikipedia.org/wiki/Global_cooling
‘Nother screenful of denialist blog pages.
Attempted critique of the Oreskes work finding that 97% of science papers which expressed a view on the causes of warming stated that human activities are a significant contributor, but it demonstrates a lack of understanding of the methodology. This is a robust finding, and newer studies have found much the same, or an even higher proportion. Among active, working climate scientists, this is not at all a controversial view.
Enforced Sunday worship gets another mention at the end, but no evidence that anyone is contemplating that was adduced anywhere in the presentation.
The presentation went for the first hour, and the remaining 30 minutes is for questions and comments. I never listen to talkback, so this bit requires even more coffee.
First question, paraphrased: “Peer reviewed journals will not even allow some papers to be published”. Um, yeah, that’s what peer review does! It is a check for quality, and makes sure that the evidence adduced and the methods used actually lead to the conclusions presented. If a paper fails peer review it is overwhelmingly because it is a bad paper, not because of bias. I mean, like any human system, peer review is subject to random errors, but there is not a systematic error where valid evidence for one ‘side’ of an issue is suppressed. That’s just conspiracy theory thinking, founded in a failure to understand how peer review actually works.
Giem’s response “we have proof that there was interference”… wanders to slide show, scans around but doesn’t find it.
LOL: 1:02 “just be aware that if you use it (the graph showing solar irradiance) you’re going to be piled on by people with other graphs that aren’t as good” – i.e., graphs that don’t show the desired answer, just the accurate answer! And this dude had the cojones to allude to ‘cheery-picking’ within the last couple of minutes!
The question, I think, is about the arctic data, vs the global data. Unsurprising for a number of reasons. The lower graph on the same slide shows a significant CO2 correlation as well.
In the end, much of the discussion is mutual agreement. No-one really challenges the claims made.
Pretty ignorant point about 1-year running averages versus longer-period running averages…
Question about longer-term climate variation, beyond the span of human civilisation. Giem hasn’t gone to long ice-core scales and so on because he’s a creationist and doesn’t accept that the planet has been around that long. Does bust out the ‘warming leads CO2‘ claim, though.
Explicit question on how AGW is ‘packaged and sold’ and how evolution is. Roy Spencer gets a shout-out in the response.
LOL – question arises about a claim from Soon that, despite increases in atmospheric CO2 the greenhouse effect has remained flat. Giem pauses for a moment and then says “Well, I think Soon sometimes overstates his data a little bit”. OK, so you already knew that when you cited Soon as an authority earlier in this presentation, right?
1:18, audience question “All the data are based on computer models…” Yeah nah. Most of the data are based on measurements. The models model multiple scenarios, and in general have under-estimated the level and rate of change. Giem doesn’t really address the question, and goes to personal (local) experience rather than to either data or modeling. When the questioner insists, Giem claims that the models over-estimated change. Simply not the case.
Question from an audience member about a graph no-one else present has seen. Question from an audience member about sea level rise. Giem ends up acknowledging that it’s happening, though he focuses on ice melt and ignores ocean thermal expansion. Does a decent job on the distinction between sea ice and land ice. Claims polar bear populations are increasing. A quick fact check shows that this is a common denialist meme, but also not supported by the best available evidence. https://www.sej.org/publications/alaska-and-hawaii/magic-number-a-sketchy-fact-about-polar-bears-keeps-goingand-going-an
Question about ‘the Pope getting scientists together next May’. Anyone know anything about this?
In the end, of course, people will choose their sources and their biases, and if you want someone who you consider to be authoritative to bolster your existing view, this presentation might be for you. But Dr Giem says right at the start that you have to go to the peer reviewed evidence, then goes to un-peer reviewed denialist blogs and web sites for the great majority of everything he cites in the entire presentation.
Along the way he actually does acknowledge that the globe is warming and that human activities are significant contributors. Why, then, spend so much time trying to impugn the data that establish those two scientific findings?
There are legitimate debates about the best mix of approaches to reducing emissions and mitigating climate change, but this presentation doesn’t really get into that in a genuine way, instead heading off into conspiracy theories about socialists and Sunday Laws.
If you’re seeking a firm foundation in understanding what’s happening with Earth’s climate and what we should do about it, I’d advise looking elsewhere.
I’ve wanted to make this video for a while: hope you find it interesting and useful.
I did make one mistake: water definitely has more options than just ‘waggling its legs’. 😉 The hydrogens can move toward and away from the oxygen, in or out of sync.
