Observations sur l’influence hydrologique de la neige dans l’Est du Canada
Bibliographic record
Abstract
This article deals with a group of 27 rivers all situated East of the 85 th meridian and South of Hudson Bay. In the first place, the author bas calculated the ratios of snow run-off during the springtime discharge. In the Maritime Provinces, the ratio obtained is only 20% of the total discharge (with a minimum of 17%) because of the rainy marine characteristics of the climate. In the Laurentian region, the ratio is close to 30% (maximum 35%) because of abundant snow precipitations and quite low summer discharge. One must note the retentional influence of lakes which display the flooding period jar beyond springtime and lower the spring ratio down to 23%. For instance, in Northern Ontario, due to losses in the marshy zones, the author bas found a ratio of only 19%. A second problem raised in this paper is the dating of the beginning of floods caused by the melting of snow. In the Southern parts of the Maritime provinces and of Ontario, the waters are high on April 5 th. In the Southern and Central parts of Québec, the flooding period begins between the 6 th and the 20 th of April. In the regions situated North of the St. Lawrence and South of James Bay, the flooding period usually begins after the 25 th of April. So the flooding period caused by the melting of snow happens later in the Northern regions. Finally, the author considers the monthly ratios of discharge during the month that knows the highest waters. Those ratios are between 2 and 3 (maximum 4.64) but they can lower down to 1.50 due to retention operated by the lakes. The month of maximum flooding extends from March, in the Southern parts of the zone covered by this study, to June, in the Northern parts. As a general rule, the figures found in this article are lower than those recorded for the rivers of the U.S.S.R.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".