Evaluating a hierarchy of snowmelt models at a watershed in the Canadian Prairies
Bibliographic record
Abstract
Three semidistributed snowmelt models (SDSM) were developed and applied to the Paddle River Basin (PRB) in the Canadian Prairies: (1) A physics‐based, energy balance model (SDSM‐EBM) that considers vertical energy exchange processes in open and forested areas, and snowmelt processes that include liquid and ice phases separately; (2) a modified temperature index model (SDSM‐MTI) that uses both near surface soil temperature (Tg) and air temperature (Ta); and (3) a standard temperature index (SDSM‐TI) method using Ta only. Other than the “regulatory” effects of beaver dams that affected the validation results on simulated runoff, both SDSM‐MTI and SDSM‐EBM simulated reasonably accurate snowmelt runoff, snow water equivalent, and snow depth. For the PRB, where snowpack is shallow to moderately deep and winter is relatively severe, the advantage of using both Ta and Tg is partly attributed to Tg showing a stronger correlation with solar radiation than Ta during the spring snowmelt season and partly attributed to the onset of major snowmelt which usually happens when Tg approaches 0°C. After resetting model parameters so that SDSM‐MTI degenerated to SDSM‐TI (the effect of Tg is completely removed), the model performance worsened, even after recalibrating the melt factors using Ta alone. It seems that if reliable Tg data are available, they should be utilized to model the snowmelt processes in a prairie environment, particularly if the temperature‐index approach is adopted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".