Half and Half: One Year Later, A Mat with 50-Percent RAP Looks Really Good
Bibliographic record
Abstract
This article describes how a stretch of asphalt in Canada that was laid down in September 2009 is beginning to reveal its secrets. The initial tests on the road included the examination of a section which used 50-percent Reclaimed Asphalt Pavement (RAP) provided by the old road that it replaced. The first results are very positive and that’s good news to the proponents of using higher percentages of RAP, a process that had environmental (fewer emissions) and budgeting (lower cost) pluses for road construction. The Asphalt Research Consortium (ARC) met with Manitoba Infrastructure and Transportation (MIT) in March of 2009 to discuss research that is being conducted by the ARC and the desire of MIT to incorporate “green” technologies into its asphalt paving construction. The ARC was working on a five-year research project funded by the Federal Highway Administration (FHWA) with significant emphasis on “green” technologies like higher levels of RAP, warm-mix technologies, and cold-mix technologies.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.094 | 0.042 |
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".