Giant natural fluctuation models and anthropogenic warming
Bibliographic record
Abstract
Abstract Explanations for the industrial epoch warming are polarized around the hypotheses of anthropogenic warming (AW) and giant natural fluctuations (GNFs). While climate sceptics have systematically attacked AW, up until now they have only invoked GNFs. This has now changed with the publication by D. Keenan of a sample of 1000 series from stochastic processes purporting to emulate the global annual temperature since 1880. While Keenan's objective was to criticize the International Panel on Climate Change's trend uncertainty analysis (their assumption that residuals are only weakly correlated), for the first time it is possible to compare a stochastic GNF model with real data. Using Haar fluctuations, probability distributions, and other techniques of time series analysis, we show that his model has unrealistically strong low‐frequency variability so that even mild extrapolations imply ice ages every ≈1000 years. Helped by statistics, the GNF model can easily be scientifically rejected.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".