CLIMATE THEORY VERSUS A THEORY FOR CLIMATE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it