Un point de vue multifractal sur l'évolution climatique
Bibliographic record
Abstract
Click to increase image sizeClick to decrease image sizeThe global warming assumption has not yet been convincingly substantiated from hydrometeorogical data analysis. In fact, as the atmosphere and the hydrosphere are highly non linear systems, one cannot expect a linear response to an increase of green house gas concentration because there exists various interactions and feedbacks at different scales between these systems and between their components. Before any prognosis about climate change it is rather indispensable to have a better knowledge of its natural variability. In any case, it will be extremely difficult or even fallacious to separate the anthropogenic and natural variability as it is likely that they strongly interact. To overcome such difficulties we argue that one has to keep as close as possible to the non linear physics of the involved phenomena. This is the objective of a multifractal analysis, which is both multiscale and multiintensity, of the available data.
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How this classification was reachedexpand
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) | 0.040 | 0.004 |
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; both teacher heads agree on what is shown here.
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".