Trends in Canadian surface temperature variability in the context of climate change
Bibliographic record
Abstract
Much of the previous work on trends in temperature extremes has considered anomalies relative to a fixed base period climatology. Calculated in this way, trends toward more extreme warm events and less extreme cold events will be found if the mean temperature is warming. In this study we calculate anomalies relative to a 30‐year running mean in order to examine trends in surface temperature variability and extremes separately from changes in the mean. The difference between trends calculated relative to a running mean and those calculated relative to a stationary mean will depend on the magnitude of the trend in the mean. Monthly trends in the positive, negative, and absolute values of daily minimum and maximum temperature anomalies at 158 stations across Canada are presented. The slopes of the trends and their significance are calculated using non‐parametric methods. Trends are strongest in winter and early spring. Decreasing variability is found in the west and northeast, with the greater part of this reduction due to less intense cold anomalies. Regions of increased variability exist in the Prairies during winter and in the Atlantic Provinces in early spring. In general, the trends in variability are small compared to the mean temperature trends, implying that while the mean of the temperature distribution at Canadian sites is warming, the variance shows little change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".