Impact of climate change on winter precipitation regimes across Ontario
Bibliographic record
Abstract
The goal of this thesis lies in the identification of possible significant trends in winter temperature and precipitation across Ontario. In this study the trends and variations in several indices of daily winter temperature and precipitation were explored applying least squares regressions and Mann-Kendall test for 13 stations located across Ontario for the period 1939 to 2008. Correlation analysis was also used to detect possible link between winter temperature and winter precipitation variables. Datasets were supplied by Environment Canada. The analysis of the temperature indices indicates a significant upward trend in winter temperature values. Also the analysis of the precipitation indices reveals no significant trend in the winter total precipitation series, decreasing trends in winter snowfall, and increasing trends in winter rainfall. This analysis showed that there has been a significant increase in the number of rainy days, but no significant trend has been detected in the number of snowy days. A downward trend in winter snowfall as a percentage of winter precipitation (S/P), and an upward trend in winter rainfall as a percentage of winter precipitation (R/P) have also been detected. The downward trend in winter S/P is linked to the decreasing trend in the winter snowfall, and the upward trend in R/P is linked to the rising trend in winter rainfall. The trends in inter rainfall and snowfall were found to be correlated with the trend in winter temperatures.
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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 | 0.002 |
| 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) | 0.002 | 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".