Development of the New 2011 Canadian Pavement Asset Design and Management Guide: A Summary of Canadian State-of-the-Practice
Bibliographic record
Abstract
The development of the new 2012 Transportation Association of Canada (TAC) Pavement Asset Design and Management Guide (PADMG) began in September 2009. The project is a pooled fund study being sponsored by 19 Canadian public agencies including all ten provincial Canadian Departments of Transportation, several major Canadian cities, several industry associations and the federal government. The effort is being led by a Canadian consortium team composed of consultants and academics from across Canada. The consortium team is composed of a diverse group of practitioners who have experience across Canada in the various areas of expertise pertinent to this Guide. The new 2012 PADMG will be an up-to-date, practical consolidation of Canadian pavement design and management practice for practicing engineers, managers and technicians. It will also be a valuable resource for college and university courses both in Canada and elsewhere. The Guide highlights key industry issues such as sustainability, climate change, and new innovations. This paper provides a brief overview of the development process and includes a summary of the key findings from a stakeholder survey conducted to determine current state-of-the-practice in Canada. Information collected in this survey covers the major practice areas in pavement design and management. The procedure for development of this Guide is also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".