Characterizing change in the variability of surface air temperature records: a comparative approach
Bibliographic record
Abstract
An accurate and comprehensive characterization of surface air temperature (SAT) variability is important for numerous purposes: studies on climate and climate change, the evaluation of climate model outputs, research on the impacts of changes in variability, etc.In this paper, SAT variability is considered from two different points of view: a measure of dispersion referring to the values in the time series, but ignoring their temporal sequence (the standard deviation); and a measure of persistence, for which the succession of the values in the time series is important (the exponent H established with Haar wavelet analysis).This paper uses daily minimum and maximum temperature records from Canadian stations in the Atlantic region and finds that: (i) SAT pattern variability can be assessed with the help of distinct methods applied together, in ways that might not be possible with any of the applied methods used separately; (ii) SAT pattern variability changes significantly over time; (iii) oscillations on scales from years to decades in both standard deviation S and the H exponent take place; (iv) the temporal change in SAT variability is reflected differently by the two applied methods: general statements concerning increases or decreases in variability should not be made without specifying the applied measure of variability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".