Snow depth simulated by BATS-SAST model and its improvement
Bibliographic record
Abstract
A BATS-SAST model was employed to simulate the snow processes in four snow cases of Sk_OJP 2001/2002, 2002/2003, 2003/2004 and Sk_HarvestJP 2003/2004 of Canada. At Sk_OJP site we modified the long-wave radiation and precipitation schemes. Considering the different interceptions between rain and snow and the effect of wind and canopy temperature on snow download, we improved the canopy interception model. At Sk_HarvestJP site we modified the snow cover fraction scheme. Results show that the model reasonably simulates the basic processes of snow cover. The modified model, which considers the part of the long-wave radiation and precipitation transmitted through the canopy at Sk_OJP site, can increase the simulation of snow depth which is closer to the observations. The improved canopy interception model, which influences the variation of snow depth under the canopy by changing canopy interception, is a great improvement on simulation of snow depth, especially on the ablation of snow cover. At Sk_HarvestJP site, there are obvious improvements on simulation of snow depth on the ablation of snow cover.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".