Precipitation trends on the Canadian prairie
Bibliographic record
Abstract
The amount and timing of precipitation on the Canadian Prairie is critical to grain production. Information on the precipitation trend is therefore vital to this region. Regression analysis was used to establish linear trends of precipitation amounts and number of precipitation events at 37 stations with 75 years of record (prior to and including 1995) across the Canadian Prairie. There has been a significant increase in the number of precipitation events mainly due to an increase in the number of low-intensity events. As such precipitation events are not getting more intense. From 1921 to 1995 on the Canadian Prairie, the number of precipitation events (excluding events that are 0.5 mm or less) has increased by 16 events, and precipitation and rainfall amounts have increased by 0.62 mm and 0.60 mm per annum, respectively. During the period from 1921 to 1960 the trends in precipitation, rainfall and snowfall were not statistically different from zero. However, from 1961 to 1995, snowfall has declined by 0.95 mm per year. The trends in the most recent period (1961 to 1995) were also significantly different from those in the 1921 to 1960 period for snowfall. The difference in trends between the two periods for snowfall, combined with the inverse relationship in the rainfall-snowfall trends suggest that these trends may be related to climate change.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".