Detecting Climate Change over Canadian Prairies
Bibliographic record
Abstract
ABSTRACT\nDetecting Climate Change over Canadian Prairies\n\nBy\nMartin Ponce\nThis research paper looks to see how rising temperatures have affected precipitation frequency in the prairies of Canada by examining day to day weather station records based on the duration of record keeping at the stations (at least a minimum of 25 years) from 1955-2015. Ten weather stations meet the criteria; two stations in Alberta, three stations in Manitoba and five stations in Saskatchewan. For each station, seasonal trends and a correlation analysis of six climatic variables are examined they include air temperature, dew point temperature, relative humidity, precipitation total, precipitation frequency and precipitation intensity. Results show that air temperature trends are increasing, dew point temperature is mainly increasing, relative humidity is increasing, precipitation total is decreasing, precipitation frequency is both increasing and decreasing while precipitation intensity is also increasing and decreasing depending on locations. Correlation seems to be strongest with dew point temperature and relative humidity while very weak with air temperature when looking at how each variable affects precipitation total, precipitation frequency and precipitation intensity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".