Observed regional climatic changes over Ontario, Canada, in response to global warming
Bibliographic record
Abstract
ABSTRACT Human‐induced climatic changes are not expected to be uniform across the globe due to the regional variations in topography, land cover/land use, economic development and so on. Investigating the regional effects of climate change is thus of great concern for decision makers and resource managers to develop scientifically informed policies and strategies against the changing climate. In the present study, regional climatology over Ontario, Canada, and its temporal trends in the past century are analysed based on the historical observations at the gauged stations, aiming to investigate how the local climate has been affected by human‐induced global warming. The analysis shows that the annual mean temperature over Ontario varies mainly between 1.6 and 7 °C with a median of 3.8 °C, while its annual total precipitation usually ranges between 836 and 1004 mm with a median of 896 mm. Further correlation analysis suggests that no or negligible correlations between total precipitation and mean temperature are found at the vast majority of stations (accounting for over 80% of the total), except for summer when significant negative correlations (with a correlation co‐efficient varying between −0.7 and −0.2) are reported at over 54% stations. As for the temporal trends, significant warming trends are detected throughout the province and the overall trend in annual mean temperature varies largely between 0.01 and 0.02 °C year–1. Increasing trends in annual rainfall (by 1–3 mm year–1) and total precipitation (by 1–4 mm year–1) are detected at the vast majority of gauged stations, but no significant trends in annual snowfall are identified at most of the stations. The results of this study can help better understand the regional climate of Ontario and provide important references for developing future climate scenarios that can be used for impact studies.
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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 | 0.003 |
| 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".