Bibliographic record
Abstract
The authors present a recently designed and ready to implement freeway incident detection algorithm based on genetically optimized Probabilistic Neural Network (PNN). The combined use of genetic algorithms and neural networks produce GAID: a Genetic Adaptive Incident Detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect incident between the said stations. As input GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it employs Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure, that is updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to offline tests using real incident data from a number of freeways in California. Results and further comparison with McMaster Algorithm show GAID with PNN core has better detection rate and less false alarm rate compared to the PNN alone and to the well established McMaster algorithm. Results also showed that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".