Climatology of and Factors Contributing to Occurrences of Near-0°C Temperatures and Associated Precipitation At and Near Terrace, British Columbia, Canada
Bibliographic record
Abstract
Occurrences of near-0°C temperatures (–2°C ≤ T ≤ 2°C) are common in cold regions such as Canada, and these conditions can lead to freeze–thaw events and hazardous precipitation. Many locations in the Coast Mountains within the Canadian province of British Columbia (BC) are especially prone to near-0°C conditions. This study examines the factors that promote anomalously persistent near-0°C conditions, often with precipitation, in Terrace, a small industrial city within the Coast Mountains of northwestern BC. The climatology of near-0°C conditions and associated precipitation over the 1956–2020 period was developed using a combined network of weather stations and field data to study the atmospheric conditions and precipitation during periods of near-0°C conditions. Events with continuous near-0°C conditions generally had long durations, with a climatological mean of 11 h, which increases substantially if accompanied by precipitation (18 h), and even more if accompanied with freezing precipitation (38 h). The longest near-0°C event lasted 233 h and was associated with long-lasting snow and rain. By combining the field data information, large-scale weather conditions and long-term climatology, several factors that contribute to near-0°C conditions at Terrace were identified. These include the ocean’s proximity, the surrounding topography, persistent cloudiness, and diabatic processes associated with melting and freezing, although local factors linked with topographic features are also important. Collectively, this study has characterized and improved our understanding of Terrace's near-0°C conditions and its associated precipitation, and these insights can be used for improved forecasting of hazardous events in the area.
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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 | 0.002 | 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".