A Long-Term Analysis of the Moose Jaw Climate Station (4015322/4015320): Temporal Trends and Frequency Analyses for Temperatures, Precipitation, and Wind Speed
Bibliographic record
Abstract
Abstract A long-term analysis of temporal trends and frequency analyses for temperatures (1913-2010), precipitation (1909-2010), and wind speed (1954-1996) was conducted on the Moose Jaw climate station in south-central Saskatchewan, Canada. Average annual and springtime temperatures are increasing over time, as are daily mean temperatures during March. Mean daily maximum temperatures are increasing on an annual basis and during the spring period, whereas mean daily minimum temperatures are increasing during February, March, August, and September, as well as on an annual basis and during spring and summer. There are significant positive time trends for growing degree days base 8C (GDD~8~) and 10C (GDD~10~). Rainfall has been increasing during March as well as during winter, and decreasing during October. Significant declines are occurring in the mean of homogeneous wind speeds during April, May, June, July, September, November, and December, as well as on an annual basis and during spring, summer, and autumn. Frequency distributions of monthly, seasonal, and annual climate variables were generated to facilitate more reliable risk analyses for agricultural activities and hydrologic modeling efforts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".