Relative changes in consistency of winter surface air temperature during ENSO events across western Canada
Bibliographic record
Abstract
Previous studies established a connection between El Niño–Southern Oscillation (ENSO) and winter surface air temperature (SAT) in western Canada. This paper compares the year‐to‐year variation of winter SAT across the region under ENSO and ENSO‐free (neutral) conditions. The comparison is based on the ratio of the difference between the standard deviation (σ) of mean monthly ENSO SAT signals and σ of mean monthly ENSO‐free signal to σ of the mean monthly ENSO‐free SAT. The signal is defined as the difference between mean monthly temperature during ENSO and the mean monthly ENSO‐free SAT. During El Niño December and February, SAT variability is lower by 13 and 18 percent, respectively, than in an equivalent ENSO‐free period. In January, variability under El Niño is 24 percent higher than its ENSO‐free counterpart. During La Niña, decrease in variability is observed during all three months by 3, 21 and 24 percent from December through February, respectively. The lower variability experienced in five of the six ENSO months underscores greater winter SAT consistency during ENSO. Variability difference is lowest along the Pacific Coast and highest in the Prairies. Except in January under El Niño, those areas, which normally experience SAT variability higher than the regional average (mostly the Prairies), achieve greater temperature stability (reduced variability) during ENSO. January variability is higher in the Prairies during El Niño. In general, in western Canada, SAT regimes look more similar under ENSO than they do under ENSO‐free conditions. ENSO tends to stabilise winter temperatures more effectively in the Prairies than it does along the coast.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".