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Old August 23rd, 2017, 12:57 PM   #41
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Read up on the "scientist" the reporter quotes. Pure bullshit again and again.

Then try reading something from real scientist. But you won't. They go against your preconceived ideas and you can't stand that.
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Old August 23rd, 2017, 09:16 PM   #42
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Old August 23rd, 2017, 09:30 PM   #43
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Read up on the "scientist" the reporter quotes. Pure bullshit again and again.

Then try reading something from real scientist. But you won't. They go against your preconceived ideas and you can't stand that.
A "real scientist" being anyone that agrees with you. Yeah, that's objective (sarcasm).
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Old August 23rd, 2017, 09:41 PM   #44
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A "real scientist" being anyone that agrees with you. Yeah, that's objective (sarcasm).
How about scientists that publish their work in real peer reviewed journals instead of Breitbart and other right wing trash sites?
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Old August 24th, 2017, 12:37 AM   #45
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How about scientists that publish their work in real peer reviewed journals instead of Breitbart and other right wing trash sites?
I read about the scientist you questioned. She has credentials and is published.
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Old August 24th, 2017, 11:04 AM   #46
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I read about the scientist you questioned. She has credentials and is published.
What credentials and published where? The Institute of Public Affairs is an Australian denier organization.

Her training was in biology.
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Old August 24th, 2017, 08:23 PM   #47
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Machine unlearning

Posted on August 22, 2017 by ...and Then There's Physics


Someone sent me a paper by John Abbot and Jennifer Marohasy called the application of machine learning for evaluating anthropogenic versus natural climate change. Their conclusion is that most of the observed warming could be natural and that the Equilibrium Climate Sensitivity (ECS) is about 0.6oC. Remember that the IPCCís likely range is 1.5oC to 4.5oC, so this is well outside the range where we would expect it to be.

So, how did they do this? They take proxy data (5 sites plus a multi-proxy for the Northern Hemisphere) and use spectral analysis to determine a set of sinusiodal variations that fit this proxy data. The output from this spectral analysis is then fed into an artificial neural network (a form of machine learning) which is then used to project the warming for the period 1880-2000 for the Northern Hemisphere and at the individual proxy sites. They find that the observed warming, since the mid-1800s, can mostly be explained as being a consequence of these natural fluctuations. The residual is then used to estimate the ECS, which they suggest is around 0.6oC.

Well, this is simply nonsense. Itís essentially just a complicated curve-fitting exercise. The average temperature of the Earth is largely constrained by energy balance. This, of course, does not mean that it canít vary, but we do mostly understand what can cause these variations. There are internal/natural cycles that can produce variations, but there are limits as to how large these internally-driven cycles can be and how long they can last. On timescales much longer than a decade, or so, we would expect these to be small, otherwise it would indicate that our climate is much more sensitive to perturbations than we expect (exactly the opposite of what this paper suggests).

Long-term (multi-decade) changes in our climate are mostly a consequence of external perturbations; volcanoes, the Sun, emission of greenhouse gases, changes in ice sheets (typically a consequence of variations in our orbit). These are all rather complex processes and the idea that one could predict how they will change in future by fitting some sine curves to a few different temperature proxy records is rather ridiculous.

This highlights the key problem with the approach in this paper; you canít try and understand what causes our climate to vary, or how it might vary in future, using machine learning alone. Even though our climate is complex, it is still a physical system and we do understand the underlying physical processes quite well. You do need to take this into account. The idea that (as the paper suggests)

[a]n alternative approach, as demonstrated here, does not require a prior understanding of the physical processes, but adequate data and appropriate machine learning techniques


is ridiculous. If you donít consider the underlying physics, then you essentially know nothing about whatís causing the climate to change/vary.

Thatís not to say that machine learning canít play a role. However, if you are going to use something like machine learning to make predictions about the future, you do need to be pretty confident that the data that you use to train the machine learning algorithm presents a reasonable representation of the system youíre trying to model. This requires some actual understanding of the system being considered. If you change it by, for example, pumping lots of greenhouse gases into the atmosphere, then the training data will almost certainly not be appropriate.

Ultimately, if your naive approach Ė that completely ignores physics Ė produces a results that is inconsistent with our understanding of the physical system (suggesting, for example, that itís almost all natural and that the ECS is about 0.6oC), then itís much, much more likely that the machine learning algorithm is producing nonsense, than there being something wrong with what is essentially fairly basic physics.


https://andthentheresphysics.wordpre...ne-unlearning/
And here is where that Abbot and Marohasy paper can be found.:

The application of machine learning for evaluating anthropogenic versus natural climate change - ScienceDirect!


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Old September 10th, 2017, 07:16 AM   #48
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Old September 10th, 2017, 07:25 AM   #49
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Didn't even read or watch it. Pretty sure that people who would write the title to say "cult-liberal have some pre-conceived prejudices. Fucking DUUUUH.....
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Old September 10th, 2017, 07:38 AM   #50
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Didn't even read or watch it. Pretty sure that people who would write the title to say "cult-liberal have some pre-conceived prejudices. Fucking DUUUUH.....
Fingers in ears, chanting la la la la, I can't hear you.

If you really don't care the best endorsement of your apathy would be not to comment.
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