First experience with application of road condition model METRo in the Czech Republic
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
Forecast of road surface conditions in the cold part of year is important for operational safety. In addition to that, such forecast can help improving and optimizing road maintenance, which may save significant financial resources. To forecast road conditions we chose the Model of the Environment and Temperature of Roads (METRo) and adopted it for use on motorways and roads in the Czech Republic. METRo is a physical model developed at Environment Canada. It evaluates the complex interactions between the ambient environment and the road surface, including the radiation budget and phase changes of any moisture on the road surface. Our version of METRo (METRo-CZ) uses on-line measurements of road weather stations and forecasts of the NWP model ALADIN which is the operational model of the Czech Hydrometeorological Institute. We applied METRo-CZ to the winter season 2012/2013 in semi-operational mode. The forecast was calculated with the most typical model setup and three other modifications. The versions differed in source of input radiation flux (either indirect calculation using total cloud cover or values provided by ALADIN) and in inclusion/skipping of a statistical postprocessing of ALADIN outputs. First results indicate that METRo-CZ can provide useful information for improving road safety and maintenance. The best results are obtained when a statistical postprocessing to ALADIN outputs is applied.
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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) | 0.000 | 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 it