First experience with application of road condition model METRo in the Czech Republic
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.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".