Comment on egusphere-2025-3979
Bibliographic record
Abstract
Abstract. In the context of climate change, stakeholders and decision makers need easily accessible bias-adjusted projections of snow cover and indices produced from those to develop adaptation plans. To meet this need, we produced an ensemble of regional climate projections statistically bias adjusted of snow water equivalent (SWE) in the province of Québec, Canada. This bias adjustment required some fine-tuning to operational methods, mainly due to the seasonality in the SWE. We calculated indices of interest for several sectors based on the bias-adjusted SWE. These indices included the maximum of SWE as well as the duration, start, and end of the snow season, and the days without snow cover. In eastern Canada, snow cover tended to persist for shorter periods as the climate warms, with symmetrical shrinkage at the beginning and the end of the snow season, with the exception of the Nunavik region. The maximum SWE was projected to decrease in the southern part of the domain and increase elsewhere. The snow season in the Côte-Nord, southern Québec and St-Lawrence River Valley regions would be increasingly interrupted by sequences of days without snow cover, whereas this would not be the case for the northern and central Québec regions.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.031 | 0.012 |
| Insufficient payload (model declined to judge) | 0.185 | 0.104 |
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".