Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One
Bibliographic record
Abstract
This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".