Accelerated Concrete Pavement Rehabilitation
Bibliographic record
Abstract
As highway agencies across the country attempt to balance rebuild existing highways while they reduce congestion and user delays and improving safety, the use of accelerated highway rehabilitation methods has become a necessity. This has been the case for the California De-partment of Transportation (Caltrans), which recently undertook a major concrete pavement rehabilitation project on I-15 near the city of Ontario, California. The I-15 Ontario Corridor carries about 200,000 ADT with 4-6 lanes each direction, about 6 percent of which is heavy trucks during peak hours. The size of the project is approximately $86 million in the engineer’s estimate cost. It is scheduled to start construction on February 2009 and to be completed by April 2010. The major scope of the project is the replacement of concrete pavement on two outside lanes in both directions along the 7.5-km (4.7-mi) stretch. Due to a complexity of con-struction access and rehabilitation process, the project was designed to implement various types of concrete pavement rehabilitation methods. Basically, the old concrete pavement will be re-placed with one of: (1) normal portland cement concrete (28-day curing-time mix); (2) rapid strength concrete (12-hour curing-time mix); (3) fast-setting hydraulic cement concrete (4-hour
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".