Developing an Overall Combined Condition Index for Pervious Concrete Pavements Using a Specific Panel Rating Method
Bibliographic record
Abstract
Pervious concrete pavement (PCP) has the potential to provide significant sustainability benefits. To understand better the significance of PCP, its performance should be evaluated comprehensively throughout its service life. Because PCP performance has not been investigated thoroughly, no condition indices have been developed for it. This paper aims to develop an overall combined condition index to investigate PCP performance. To develop the index, a specific panel rating method and field performance investigations were conducted. The panel subjectively evaluated surface conditions and permeability rates of various PCP sections. The field investigations involved objective measurements of surface conditions and permeability rates of the same PCP sections. It is not practical to conduct a panel rating for PCP condition evaluation in the future; conducting field investigations (objective assessments) is more practical and cost-effective. Thus, regression analyses were applied to relate objective surface condition measures to subjective surface condition ratings. After objective surface condition measures were evaluated, subjective surface condition ratings were computed using the regression relationship. Permeability rates were combined with subjective surface condition ratings using adequate weighting factors to obtain the overall combined condition index.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".