Local calibration of flexible pavement performance models in Michigan
Bibliographic record
Abstract
The performance prediction models in the Pavement-ME are nationally calibrated using in-service pavement material properties, pavement structure, climate and truck loading conditions, and performance data obtained from the Long Term Pavement Performance program. Generally, the nationally calibrated models may not be accurate if the inputs and performance data used to calibrate do not represent a state’s local conditions and practices. Therefore, each state highway agency (SHA) should evaluate the nationally calibrated performance models to determine the adequacy of predicted field performance before implementing the new M-E design procedure. If the predictions are not satisfactory, local calibration of the Pavement-ME performance models is recommended to improve the performance prediction capabilities reflecting the unique field conditions and design practices. The commonly used calibration technique such as split sampling does not necessarily provide adequate results, especially with small sample sizes. Consequently, there is a need to employ statistical methodologies that are more efficient and robust for model calibrations given the data related challenges encountered by SHAs. The bootstrap is a nonparametric and robust resampling technique for estimating standard errors and confidence intervals of a statistic. The main advantage of resampling methodologies like bootstrapping includes estimation of a parameter without making distribution assumptions. This paper presents the use of resampling techniques to locally calibrate the flexible pavement performance models and how the locally calibrated Pavement-ME models improved the performance prediction accuracy. The results of the local calibration show that the validation standard error and bias obtained from bootstrapping were much lower than other resampling techniques. In addition, the validation statistics were similar to that of the model calibration, which indicates robustness of the local model coefficients. The reliability equations after local calibration are a better representation of measured pavement performance in Michigan.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".