Bibliographic record
Abstract
Asphalt pavement recycling contributes to sustainable transportation infrastructure through reduced natural resources requirements (aggregates, asphalt binders and fuels) and environmental impacts. Practical Canadian, American and Colombian applied design, materials and construction experience with hot in-place recycling (HIR), cold in-place recycling (CIR) and full depth reclamation (FDR) is used to illustrate the processing features, overall project design procedures, construction requirements, appropriate specifications, quality control/quality assurance and anticipated flexible pavement performance involved. Third generation HIR of functionally deteriorated, but still structurally sound, asphalt pavements is a cost-competitive alternative process, of potentially equivalent performance to hot-mix asphalt (HMA) overlays. The performance of quality CIR and FDR has been positive and cost-effective, particularly for rutting resistance and reflective cracking mitigation. Innovations with CIR and FDR, such as improved mix design procedures, lime use, laboratory characterization, higher heavy traffic levels use, mechanistic pavement design parameters, including temperature relationships, incorporation in long-life asphalt pavement structures and probabilistic life-cycle costing, are presented with implementation guidance. For the covering abstract of this conference see ITRD number E215163.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".