Trend analysis of winter rainfall over southern Québec and new Brunswick (Canada)
Bibliographic record
Abstract
Abstract Winter rainfall is a non‐negligible issue for urban drainage in Canada as it can generate significant flooding, especially when it occurs at the same time as high air temperature and in the presence of an appreciable snow cover. According to climate change scenarios, it is expected that the occurrence of these events will increase in a future climate. The purpose of this paper is to perform a trend analysis on six indices related to winter rainfall (January–February) at 60 weather stations located in southern Québec and New Brunswick (Canada) in order to detect possible trends in the frequency or intensity of winter rainfall events during the twentieth century. Datasets were provided by Environment Canada and come from the Canadian Daily Rehabilitated Precipitation Database. The bootstrap‐based Mann‐Kendall test is used to detect possible non‐stationarities in the dataset, while Sen's slope estimator is used to quantify the magnitude of the slope. Results show that 19 stations out of 60 present a significant trend (18 of them being positive) at a 5% level for winter (January–February) total rainfall. In most cases where a trend was detected for winter rainfall there was also an increase in the number of days with rainfall (42% of the stations). These results suggest that globally, for the region under study, rainfall during January and February was more likely to occur, often resulting in a significant increase in the total rainfall during these months. Increasing trends in maximum daily rainfall during January and February were also observed for 9 stations (15% of the stations). The spatial distribution of stations where significant trends were detected is consistent with the hypothesis that trends in winter rainfall are more likely to be observed for stations located in the southern part of the region under study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".