Estimation des débits sous glace dans le sud du Québec : comparaison de modèles neuronal et déterministe
Bibliographic record
Abstract
Real-time assessments of stream flows under ice cover, using two distinct objective approaches, were compared over a 5-year period. Approaches based on artificial neural networks, defining mathematical relationships between stream flow, water level, and air temperature, and on a deterministic hydrological model were applied at eight gauged sites located in southern Quebec. Good results were obtained using both approaches, when no snowmelt contributes to the rise of the inflows. In the other hydrological situations, the neural network results were the best, but results of both approaches were sensibly poorer. Nevertheless, the potential for increasing the skills of the deterministic model seems high. Otherwise, a preliminary analysis showed that both approaches lead to stream-flow estimations that are not radically worse than the ones performed by the team of experts.Key words: discharge measurement, hydrology, ice-affected stream flow, hydrological modeling, neural network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".