Change in Winter Precipitation Regime across Ontario, Canada
Bibliographic record
Abstract
The focus of this study is to investigate the effects of climate change on the hydrologic regimes in Ontario, Canada. The variables include total precipitation, the form of precipitation (snowfall and rainfall), and the temperature during winter. The winter season is hydrologically significant for Canadian conditions. The historical data for 70 years, from 1939 to 2008, on total precipitation, snowfall, rainfall, and temperature over the winter period were analyzed using least-squares regressions, Alexandersson’s Standard Normal Homogeneity Test, and the Mann–Kendall test for 13 stations across Ontario to identify positive and negative trends and their significance. The analysis of the precipitation indices reveals no significant trend in the winter total precipitation, decreasing trends in winter snowfall, and increasing trends in winter rainfall. The snowy day analysis depicts a large scatter across the province, with the number ranging from 40 days to 80 days, which shows that the number of snowy days varies considerably over the years at all stations. The analysis showed that the change in snowy-rainy days is attributed to the significant upward trend of the daily mean winter minimum temperature for almost all the stations. Therefore, the changes in the form of precipitation during winter may affect water management including streamflow, tile drainage flow, soil erosion, sediment and nutrient transport to surface water bodies, and the effectiveness of best management practices being used for managing non-point source pollution.
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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 | 0.002 | 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".