Evaluating Innovative Pavement Technologies Through Laboratory and Field Testing
Bibliographic record
Abstract
New and improved pavement technologies are developed through laboratory investigations, construction and maintenance, theoretical analyses, long term performance studies such as SHRP and C-SHRP, and integrated programs of laboratory and field research. It is the latter which is the subject of this paper. In 2002 the Canada Foundation for Innovation (CFI), Ontario Innovation Trust (OIT), Ontario Research and Development Foundation (ORDCF), and several private and public sector partners provided $9 million in total in research funding. The first major initiative to occur under this support is an integrated laboratory and field research program involving new state-of-the-art testing equipment, and new expanded field and central laboratories and central and satellite field test sections. This paper concentrates on how the test track is being used in combination with the laboratory to improve Canadian pavement design, construction and management. Results obtained from the central laboratory in combination with the field test track and satellite test tracks are providing useful information for CPATT partners. Furthermore, they are providing an excellent opportunity to train and educate a new generation of pavement engineers. The paper also provides several conclusions and recommendations on the future opportunities for further advancements in pavement technologies. For th ecovering abstract of this conference see ITRD number E215163.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".