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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".