L’enneigement hâtif des monts Valin et l’effet du lac Saint-Jean : une analyse météorologique
Bibliographic record
Abstract
L’influence météorologique que peut avoir une masse d’eau sur les terres avoisinantes est un sujet connu. Néanmoins, afin d’en savoir un peu plus, voici un cas particulier au Québec concernant le lac Saint-Jean. Par temps froid, ce dernier, encore à l’eau claire, pourrait contribuer aux premières chutes de neige avant l’arrivée de l’hiver comme tel. C’est ce que cette étude tente de démontrer en faisant intervenir le jeu combiné de la convection atmosphérique sur le lac et de l’ascendance orographique de l’air sur les monts Valin. L’air humide au-dessus du lac, poussé par les vents favorables, se transforme en flocons et en chutes de neige rendues en altitude. L’analyse des données météorologiques de six stations permet de présenter des conditions spécifiques de temps et de dégager des moments et des modèles dans lesquels vraiment les chutes de neige en montagne dépassent largement ce qui se passe dans la plaine en amont. The meteorological influence that a body of water can have on neighbouring land is not entirely unknown. Nevertheless, to learn a little more about this, here is a specific case in Quebec concerning Lake Saint-Jean. In cold weather, could this lake, with its clear water, contribute to the first snowfalls before the arrival of winter proper? This is what this study attempts to demonstrate. It does so by considering the combined effects of atmospheric convection over the lake and orographic uplift of air over the Valin Mountains. The humid air above the lake, pushed by favourable winds, turns into snowflakes and snowfall at high altitude. Analysis of meteorological data from six stations makes it possible to present specific weather conditions and identify times and patterns in which snowfall in the mountains greatly exceeds what occurs in the plains upstream.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".