Bibliographic record
Abstract
The autocorrelation function (ACF) and its relationship to fluctuation analysis (FA) are discussed, based on the reanalysis monthly mean geopotential height at 500 hPa from ECMWF (ERA-20C). ACF provides a measure of the influence of correlation at different time points. The principal pattern ACF shows relatively large positive correlations between every consecutive years in the central Asian continent and the North African continent. However, the confidence intervals of the ACF distributions are generally too wide, and so of low confidence level. It is found that the scaling rule of the ACF breaks down typically, while the distributions of the critical exponent of FA always show patterns. It is also found that the larger values of critical exponent appear in the tropical region, which reflects a larger climate persistence. The critical exponent becomes smaller in the jet stream regions, indicating less year to year reappearance. The characteristic time accounts for the weighted summation of all possible ACFs, which represents a memory length of a certain climate events. In the boreal winter, some robust patterns can be identified by the corresponding distribution of the characteristic time, which provides a unified way of describing the persistence of climate. An approximate relation between the critical exponent of FA and the characteristic time has been found, and such relation is verified by the distributions of these two physical variables.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".