Bibliographic record
Abstract
Snow is the main source of streamflow in temperate regions characterized by very cold and snowy winters. Due to global warming, these regions are experiencing a significant decrease in snowfall. The main objective of this study is to analyze the impacts of snowfall on the spatio-temporal variability of mean annual flows (MAFs) of 17 rivers, grouped into three hydroclimatic regions, from 1930 to 2019 in southern Quebec. In terms of spatial variability, snowfall is the variable most correlated with MAFs (positive correlation), followed by drainage density (positive correlation) and wetland surface areas (negative correlation). Due to the influence of these three factors, MAF values are generally higher in the most agricultural watersheds of the southeastern hydroclimatic region on the south shore than in the less agricultural watersheds of the southwestern hydroclimatic region on the north shore of the St. Lawrence River. As for temporal variability, the four statistical tests applied to the hydrological series detect no significant downward trend in MAFs, despite having reduced snowfall. Instead, they suggest an evolution toward an increase in mean annual flows, as a result of increased rainfall due to the increase in temperature. This evolution is more pronounced on the north shore than on the south shore, likely due to the presence of wetlands and others water bodies, whose runoff water storage capacity does not change over time to be able to store the surplus of the quantity of water brought by the increase in rain.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".