Bibliographic record
Abstract
TAKLE: Thank you for those kind words.So we are going to jump right into the background, and not being presumptuous in that you studied this, you have probably seen fragments of this, but I am trying to pull this together, the core of why we understand climate change and the significance of it.So I am going to be going in two sections here.We'll look, first of all, at fundamental science, and then we will look at some of the issues and impacts so we can look at some of the factors that are going to be driving the impacts and things that we have to think about trying to develop resilience to, because some of these are going to be very serious, and we will get into that.We have a lot of good foundational documents to draw on, to look at both the science and the impact.So for instance, the intergovernmental panel on climate change issues, which is about every five years, state of the climate on the global scale and an update on the science of climate change, and so we have the 2004 issue of that.And then, we have national documents that parallel the international document.The one for Canada is put out by Natural Resources Canada, and so that's an updated document that you have at your disposal.In U.S., we have two documents, one that was issued about a year ago which covers the science of climate change.So it is just the IPCC document and then updates and focuses on the science for the U.S.And then, the one that was just issued the day after Thanksgiving was the fourth national U.S. climate change assessment, and I was involved in that one as well.So we will look at some of the fundamentals of why we have this issue, and then, we will look first globally, and then we will look at North America and a few words about the Great Lakes.Well, the clim --when we talk about the climate system, we are really talking about land, ocean, atmosphere, and ice masses.Those are the four components of the climate system, and energy moves between these and among these reservoirs then.And so to understand the climate system, we have to understand how energy and mass is moved among these reservoirs.So ice melts, and it takes energy to melt ice.So part of this increase in energy that we are seeing is used to melt ice.And so that's the way we look at it.We use the same laws of physics to build airplanes, to build nuclear power plants, and we have confidence in these laws.Because we ride in airplanes, we have confidence.We can live in the vicinity of nuclear power plants because we know --we use the laws of physics to design these.These same laws are used to look at our climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.047 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".