Performance Summary Of Precast Concrete Inlay Panel Trial In Ontario
Bibliographic record
Abstract
In order to address deep-seated rutting issues on high-volume asphalt highways in Ontario, Canada, the Ministry of Transportation of Ontario (MTO) was interested in the design and testing of a Precast Concrete Inlay Panel (PCIP) trial section. The PCIP was used to increase the stiffness of the pavement section within the short overnight construction windows that the MTO specifies to minimize the effects on road users. The trial section was designed to include three separate methods of panel support, which is understood to be a primary consideration in the performance of precast concrete panels. The trial section was constructed in September 2016 on the traffic lane of Highway 400 and has been in continuous service since that time. Instrumentation was installed during construction to measure the pressure, moisture, and temperature conditions at the interface between the panels and the existing asphalt layer. This paper summarizes the performance of this trial during the more than three years of service, including findings from the sub-panel instrumentation, falling weight deflectometer testing, and condition assessments. Additionally, the findings of an analysis of the construction aspects of the different support conditions are included to reinforce recommendations regarding the best support technique for future applications of the PCIP rehabilitation technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".