Validation Study of Road Surface Water Film Depth Prediction Model
Bibliographic record
Abstract
Surface water film thickness is one of the main factors, which affect the vehicle safety on slippery roads. Water film depth is influenced by rainfall intensity, grades, cross slopes, drainage length and pavement texture. This paper reviews the research status and makes some comparative analysis of several pavement water film depth prediction models. An experimental validation has verified and calibrated the existing water film depth prediction models results. The experimental validation of the variable in the slope water flow model has been implemented by means of a small scale physical road model in a rainfall simulator, which is constructed in a laboratory. The results of comparative analysis have shown that in the existing water film depth prediction models, the regression models predict values are more closely than mathematical-physical models. Because under different experimental conditions, the regression model calibration parameters are different. In the case of specific road characteristics for prediction of water film thickness, the model parameters can be calibrated to further improve predicting accuracy.
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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 | 0.001 | 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 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".