Bayes Linear Regression Performance Model Depending on Experts’ Knowledge and Current Road Condition
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
Abstract Strategic and long-term planning in pavement management systems relies primarily on performance prediction models to ensure efficient and forward-looking management and to set present and future budget requirements. In many developing countries, roads face increasing damage because of the lack of regular maintenance. This reinforces the need to develop a system to predict the deterioration of roads in order to determine the optimal intervention strategies for the road network. This article suggests a Bayesian regression method to develop a performance model for cases when archived pavement data are not available, and this would use expert knowledge as a prior distribution. As such, experts who have worked for a long time with the road and transportation agencies have been interviewed to develop a portion of the input data. Posterior distribution was calculated using the likelihood estimation function based on road condition inspections according to the predefined protocol. The results were prediction models of pavement deterioration based on a mixture of a few onsite inspections interacting with expert knowledge.
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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 it