Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it