Bibliographic record
Abstract
While famous theoretical work has been done historically on climate, no precise testable physical theory for climate has ever emerged. That is because, among other reasons, the definition of the objective is imprecise. The most common definition of climate as averaged weather, is more cliché than definition. Average over what? Average in what way? Is there a function relating resulting averages to each other, or do the averages satisfy differential equations? There is not one but many divergent approaches to defining climate in terms of averages, which seem to coexist without mutual competition. The three primary approaches employ time averages, field averages, and model solution ensemble averages, respectively. Each is problematic in its own way. While it is easy to produce an average, finding equations that can stand on their own in terms of averaged quantities only is not straightforward. But such equations are the ultimate aim of a search for a theory of climate, examining the questions of what averaging rule over what physical quantities help point to what an actual theory for climate ought to be like. This paper discusses averaging and closure in other fields, such as kinetic theory and turbulence, and how they are relevant to a theory of climate. It suggests how we might learn from them, while identifying how these issues need more exploration in terms of the climate problem.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".