Projected Climate Impacts on Snow Depths and Discharges In the Lake Winnipeg Watershed
Bibliographic record
Abstract
A number of studies have documented recent trends toward earlier spring snowmelt (e.g. Brown, 2000) and a decline in snow cover extent (Dery and Brown, 2007) across many regions of the Northern Hemisphere in response to enhanced spring warming. Snow cover is anticipated to decrease in the future due to global warming, as snow cover formation and melt are closely related to a temperature threshold of 0oC. The hydrologic regime of the Lake Winnipeg watershed (LWW), Canada, is dominated by spring snowmelt runoff which accounts for more than 80% of the total annual surface runoff in the region, despite the fact that snowfall only contributes one third of total annual precipitation (Gray and Landine, 1988). Thus, spring snowmelt runoff plays an important role in the agricultural water supply of the region. This study investigated the Canadian regional climate model (CRCM4) future projections of precipitation and temperature as well as maximum snow depth, snow cover duration and snowmelt runoff from the North American Regional Climate Change Assessment Program (NARCCAP) database to assess the potential hydrologic impacts of climate change over the Lake Winnipeg watershed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".