Road weather forecasting – ICEWARN model
Bibliographic record
Abstract
We have developed a new model (ICEWARN) for the forecast of road surface temperature and road surface conditions. The model stems from the Model of the Environment and Temperature of Roads (METRo) developed by the Environment and Climate Change Canada. ICEWARN is linked to measurements of the road weather stations in the area of interest and to forecasts of the numerical weather prediction model ALADIN, which is the operational model of the Czech Hydrometeorological Institute.\n\nICEWARN is focused on forecasts in urban areas. It differs from the METRo model mainly in the parametrization of radiation fluxes and in the inclusion of sky-view factor for the direct solar irradiance. Besides deterministic forecasts, ICEWARN allows probabilistic forecasting of the road surface temperature based on our ensemble forecast method.\n\nAn evaluation of the ICEWARN model forecasts for selected roads in Prague during the winter season\n2016/2017 is presented. The probabilistic forecast is performed for the lead times up to 6 hours. The deterministic forecast is computed and evaluated for the lead times up to 24 hours.\n\nThe target users of the project output, which are the road maintenance services in the capital city of Prague, will obtain operational information that will enable them, in addition to reducing the weather risks, to make their winter activities as well as the whole Prague transport economically more effective and more environmental-friendly.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".