Bibliographic record
Abstract
Current asphalt concrete pavement mix type selection protocols, developed by Alberta Transportation in the late 1980s, replaced an informal system of selecting aggregate properties, asphalt cement grades, and Marshall mix design requirements. Subsequent to their initial development and implementation, several significant developments in pavements engineering (e.g. the Strategic Highway Research Program, updating of Equivalent Single Axle Load (ESAL) factors, and the implementation of segregation specifications) have occurred. This report presents the results of a research study carried out to validate the current mix type selection criteria. A network analysis of the rutting performance of over 6,600 km of primary highways representing 365 paving projects between 5 and 15 years in age formed the basis for the analysis. New protocols and criteria for the selection of mix types, based on design traffic and climate zone for typical highway loadings and conditions, are presented. Mix types are defined by aggregate and mix design criteria to primarily address high temperature rutting performance and durability. A separate protocol was developed for selecting both conventional and Performance Graded (PG) asphalt binders to address low temperature
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.818 | 0.751 |
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".