Evaluation of Canadian Climate Information and Its Effect on Pavement Performance Through MEPDG Prediction
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
This paper aims to investigate the quality of recently developed Canadian climate data and the effects of climate on flexible pavement performance using the Mechanistic-Empirical Pavement Design Guide (MEPDG). Two-hundred and six (206) weather stations were categorized into six weather zones to better understand climate distribution. The analysis was carried out for all weather stations across Canada, and sensitivities of pavement performances in terms of the International Roughness Index (IRI), Asphalt Concrete (AC) rutting and total pavement deformation were studied. Pavement performances of weather stations in close proximity were studied to investigate the consistency of Canadian climate data. Also, pavement performances for Virtual Weather Station (VWS) data and actual station data were compared.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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