A Look into Changes in Precipitation Types Linked with Surface Air Temperature over British Columbia, Canada 1953-2005
Bibliographic record
Abstract
This study attempts to understand the effects of air temperature on the frequency of snow days, rain days, and frozen rain days in British Columbia Canada. This was conducted by using historical climate observations among 12 stations from 1953 to 2001. A simple linear correlation analysis was performed to derive any changing trends and their associations with the surface air temperatures. Surface air temperature in the region has been consistently increasing over the 48 years, consistent with the global temperature trend. There also appeared to be consistent inter-annual temperature variability among these twelve stations. The annual frequency of snowfall days has been decreasing in all the twelve stations observed. It appeared that annual frequency of rainfall days did not display a clear pattern and no conclusion could be made on its connection to air temperature. The frequency of frozen rain days has increased in 10 of the 12 stations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".