Operational analysis of the spatial distribution and the temporal evolution of the snowpack water equivalent in southern Québec, Canada
Bibliographic record
Abstract
A technique for obtaining an operational regional analysis of the temporal evolution of the snowpack water equivalent in southern Québec (Canada) is proposed and implemented on a 0.1° grid. The technique combines the output of the snowpack model included in the HYDROTEL hydrological model, forced by observed temperatures and precipitations, with observed snow survey data. A strategy based on observed snow density, snowpack water equivalent and streamflow is used for model calibration. A comparison of various calibration strategies showed that the same model parameters can be used for the whole of southern Québec. It was also shown that, for operational purposes, it is sufficient to rely solely on automatic stations and to use 3 h time steps. Because snow surveys are made in deciduous forests, model parameters were adjusted to account for open areas and coniferous trees by comparing observed and simulated streamflow, using all components of the hydrological model. An assimilation technique which updates simulated water equivalent and snow density at grid points from the available snow survey data completes the operational system. An example of spring streamflow simulated using the proposed snow analysis illustrates the usefulness of the technique.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".