Local calibration of flexible performance models using maximum likelihood estimation approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The pavement mechanistic-empirical design (PMED) is a widely used pavement analysis and design approach. The transfer functions are generally calibrated to implement the PMED for local conditions. The least square (LS) approach has been commonly used to calibrate these transfer functions. Although LS is a simplistic approach, the assumptions may not be valid, especially for non-normally distributed data. This paper uses the maximum likelihood estimation (MLE) approach to calibrate bottom-up cracking, total rutting, and international roughness index (IRI) transfer function for flexible pavements. The results show that overall, MLE outperforms the LS approach for synthetic and measured data. The difference is more evident in the case of bottom-up cracking data, which does not follow a normal distribution. Gamma distribution for bottom-up cracking and total rutting, whereas negative binomial for IRI is the most suitable distribution for the MLE approach. Overall, MLE using resampling methods provides a robust and better estimate than the LS approach.
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.
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 | 0.001 |
| 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 it