Fully Bayesian Approach to Investigate and Evaluate Ranking Criteria for Black Spot Identification
Bibliographic record
Abstract
The fully Bayesian (FB) approach for identification of collision black spots has been available for some time. However, little research has been conducted on the performance of the FB method, especially on criteria for ranking sites. A study was done to fill this void by a thorough evaluation of the FB method for black spot identification. First, an investigation compared the FB approach with the now-traditional empirical Bayesian method. It was confirmed that the FB method was superior for key ranking criteria [the posterior Poisson mean (PM) of crash frequency and potential for safety improvement] based on evaluation criteria, including sensitivity and specificity, and the sum of the PM. Next, eight ranking criteria, which included PM, posterior expected, mode and median ranks, and probability of being the worst, were proposed and evaluated for the best of several FB model variations explored. The mode rank of the posterior distribution of the Poisson mean proved to be the most promising because it tended to provide the best results, especially for top-ranked sites. The sum of the Poisson mean was also found to be a solid evaluation criterion, especially for limited numbers of top-ranked sites.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".