Bibliographic record
Abstract
A BATS-SAST model was employed to simulate the snow deposition processes in the four snow deposition cases in Canada,i.e.,Sk_OJP 2001/02,2002/03,2003/04 and Sk_HarvestJP 2003/04. At Sk_OJP site the long wave radiation scheme and precipitation scheme were modified. Considering different interceptions between rain and snowfall and effect of wind and canopy temperature on snow download,the canopy interception model was improved. At Sk_HarvestJP site the snow cover fraction scheme was modified. Results show that the model is able to simulate the basic processes of snow cover reasonably. The modified model,which considers the part of the transmitted through the canopy in computing long wave radiation and precipitation at Sk_OJP site,can make the simulation of snow depth more and closer to the observation. The improved canopy interception model,which influences the variation of snow depth under canopy by changing canopy interception,has a great improvement on simulation of snow depth,especially on the ablation period of snow cover. At Sk_HarvestJP site,the snow depth was lessened simulated by the improved 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) | 0.001 | 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".