Pavement temperature model for Canadian Asphalt Binder selection: Introduction to the CPT model
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
The selection of a climate-appropriate asphalt binder is essential in ensuring the longevity of pavement surfaces. As the selection methodology depends on the pavement's temperature, several models predict pavement temperatures based on recorded ambient air temperatures and other related factors. A commonality between the most predominant pavement temperature models is the geographical limitations to their application. As a result, widely used models such as LTPP and SHRP do not return accurate values for more Northern temperatures, as observed in Canada. Thus, a new pavement temperature model has been developed for use across Canadian pavements. The resulting model returned satisfactory accuracy upon validation and represented a good alternative for Canadian pavement designers. Additionally, a software tool title CABS has been developed to select asphalt binders based on the predicted pavement temperatures to implement the model.
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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 | 0.001 | 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 | 0.001 | 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