Bibliographic record
Abstract
In 1989, the C-SHRP (Canadian-Strategic Highway Research Program) launched a national full scale field experiment known as the Canadian Long-Term Pavement Performance (C-LTPP) program. Between the years, 1989 and 1992, a total of 24 test sites were constructed within all ten provinces. Each test site contained multiple monitored sections for a total of 65 sections. Each of these sites received rehabilitation treatments of various thicknesses of asphalt overlays. The majority of the overlays used Hot-Mix Asphalt Concrete (HMAC). Several sections used HMAC with the addition of polymer, or a high friction mix, and several others used Reclaimed Asphalt Pavement (RAP). The C-LTPP program attempted to design and build the test sections across Canada so as to cover the widest range of experimental factors such as traffic loading, environmental region, and subgrade type. The environment types include Wet-No Freeze, Wet-Freeze and Dry-Freeze. This ensured that C-LTPP would encompass the majority of conditions under which pavements are constructed in Canada. It would then be possible to compare results obtained at different test sites (i.e. across traffic levels, environmental zones, soil types) by using a statistical analysis of the factorial population. The objective of this report is to assist in providing guidance on the future C-LTPP database. The C-LTPP database needs to be an up-to-date, practical consolidation of pavement data, which can be used by researchers and practitioners for pavement design with potential usage in AASHTOWare DarWin-ME Canadian calibration and management of road networks. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.019 |
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".