Preliminary Findings from Saskatchewan's Asphalt Rubber Project
Bibliographic record
Abstract
Use of crumb rubber as an additive in asphalt concrete mixes has been predominantly limited to milder climates and the performance of asphalt rubber pavements in Canada has been variable. Saskatchewan Ministry of Highways and Infrastructure (MHI) was designed and constructed an asphalt rubber pavement on Highway 11, near Findlater, as part of a rehabilitation strategy for this structural asphalt concrete pavement highway. Test sections were constructed in July 2005 to evaluate the effectiveness of the rubber asphalt mix as a rehabilitation overlay treatment for typical Saskatchewan asphalt concrete pavements, in comparison to the dense-graded asphalt mixes used by MHI. Laboratory testing was carried out to complement the field evaluation, and to quantify the mechanistic properties of the asphalt rubber mix. As well, structural field testing was performed to evaluate in-situ properties of the asphalt rubber and the conventional densegraded asphalt mix. To date both the conventional mix and the asphalt rubber are performing well in the field. Based on the laboratory characterization results, minimal difference in material properties was observed between the conventional asphalt mix and asphalt rubber mix at 20oC. Additionally, the non-destructive structural characterization results indicate that to date the conventional mix and the asphalt rubber are performing comparably in terms of deflections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".