Simulation of snow cover formation and melt with publication of the output data on the web map service (on the example of Kama river basin)
Bibliographic record
Abstract
Abstract It is performed an assessment of the use of daily precipitation forecasts of global numerical weather prediction (NWP) models GFS (U.S.), GEM (Canada), SLAV (Russia) and ICON (Germany) as input data for snow accumulation and melt modelling in the Kama river basin for two cold seasons. It is shown, that maximum snow water equivalent (SWE) calculated on the basis of NWP models output has an error less than 27% of the measured values, in the conditions of 2017-2018 snow accumulation season. However, this is preliminary assessment, which requires verification by several seasons. It is rather difficult to conclude which model provides highest accuracy of SWE calculation, because each of them has its specific limitations. In 2018-2019 cold season, we additionally obtained ICON model data, which provides the most accurate forecast of precipitation. The simulated SWE and meltwater outflow data are published on the online web map service.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".