Analysis of the Joint Link between Extreme Temperatures, Precipitation and Climate Indices in Winter in the Three Hydroclimate Regions of Southern Quebec
Bibliographic record
Abstract
We analyze the relationship between four climate variables (maximum and minimum extreme temperatures, rainfall and snowfall) measured in winter (December to March) at 17 stations from 1950 to 2000 in the three hydroclimate regions of southern Quebec, and six seasonal climate indices using canonical correlation analysis (CCA) and the copula method. This analysis yielded these major results: (1) extreme temperatures are not correlated with the amount of winter rain or snow in southern Quebec; (2) winter seasonal climate indices show better correlations with climate variables than do fall climate indices; (3) winter extreme temperatures are best correlated (positive correlation) with the Atlantic Multidecadal Oscillation (AMO) in the eastern region, but show a negative correlation with the Arctic Oscillation (AO) in the southwestern region; (4) the total amount of winter snow is best correlated (negative correlation) with the Pacific Decadal Oscillation (PDO) in the three hydroclimate regions; (5) the total amount of winter rain is best (negatively) correlated with PDO in the eastern region, but shows a positive correlation with AO in the southeast region. Finally, the copula method revealed very little change in the dependence between climate indices and climate variables in the three hydroclimate regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".