Seemingly Unrelated Regression Equations for Developing a Pavement Performance Model
Bibliographic record
Abstract
<p>The paper presents a joint analysis of some pavement performance indicators based on a system of seemingly unrelated regression equations (SURE) which allows to handle correlated error terms. In particular, three major indicators such as the side friction coefficient (SFC20°C), mean-profile depth (MPD), and international roughness index (IRI), were measured in a case study and subsequently used in analysis. Regression parameters were estimated by the Maximum Likelihood Method and the t-statistic was considered to show the statistical significance of regression coefficients. The results show that estimation points have the signs expected: the SFC<sub>20°C</sub> decreases as the number of accumulated trucks (<em>N</em><sub>t</sub>) increases; whereas the MPD and IRI increase as the number of trucks increases. A likelihood ratio test was also carried out to determine whether the system model, which assumes correlation among error terms, was more appropriate than separate models. In this particular case, with three degrees of freedom, was found that the result corresponds to a p-value 0.150 and the null hypothesis cannot be rejected at any significance level less than this value.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".