Climate modelling without irrelevant weather details: The Half-order Energy Balance Equation
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
Uncertainties in conventional numerical climate models –as measured by the spread between competing models - have for the first time in over four decades increased (IPCC, AR6, 2021). This approach is in crisis and the community is increasingly turning to Machine Learning i.e. to black boxes that “emulate” the standard (nearly) black box climate models. The root problem is that these models are based in the weather regime, i.e. they spend almost all their effort calculating irrelevant weather details. In this paper we summarize recent developments in a multidecadal effort to develop new models focused on the relevant details. We focus on the Half-order Energy Balance Equation (HEBE), that currently is the most promising candidate. It is based conservation laws (energy) and scale symmetries (scaling) and can already make low uncertainty hindcasts and projections to 2100.
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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 | 0.000 | 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