Shifts in air temperature and high-magnitude winter precipitation events in coastal North America: Implications for Environmental Assessment and Management
Bibliographic record
Abstract
Abstract This commentary examines recent shifts in air temperature data coinciding with high-magnitude precipitation events at climate stations spanning an elevational and longitudinal gradient on the south coast of British Columbia, Canada. Results presented show that high-magnitude winter precipitation events are occurring on British Columbia's south coast under progressively warmer conditions. In the future, proportionally more winter precipitation is anticipated to report as rainfall versus snow, and over time these changes will have a marked impact on the snowmelt-dominated hydrographs that characterize local watersheds. Robust preparedness strategies will be needed to balance competing interests such as the security of domestic water supplies, the permitting and operation of major projects (e.g., mines, hydrodevelopments), and the achievement of broader ecosystem health goals under these changing hydroclimatic conditions. Integr Environ Assess Manag 2018;14:185–188. © 2018 SETAC Key Points Large atmospheric river events can result in rain-on-snow conditions and widespread flooding, leading to significant property and infrastructure damage estimated in the tens of millions of dollars in coastal North America. Continued and upward shifts in air temperature are predicted for the future, and extreme precipitation events over most midlatitude land masses are very likely to become more frequent and intense as global mean surface temperature increases. Collaborative policy frameworks and robust preparedness strategies will be needed to balance competing interests under these changing hydroclimatic conditions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".