Statistical and Genetic Algorithms Classification of Highways
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
This paper reports the results of experiments comparing a conventional statistical method and an evolutionary genetic algorithms approach for classifying highway sections that is based on temporal traffic patterns. Traffic patterns are used as surrogates of two important characteristics of a highway section, namely, trip purpose and trip length distribution. Accurate classification can lead to better traffic analyses, such as estimations of annual average daily traffic volume and design hourly traffic volume, and determination of maintenance and upgrading schedules. Modern-day computers cannot solve the problem of obtaining optimal classification corresponding to minimum within-group error. The hierarchical grouping method provides a reasonable approximation of the optimal solution. However, for smaller numbers of groups, the hierarchical approach tends to move farther away from the optimal solution. The genetic algorithms based approach provides better results when the number of groups is relatively small (e.g., less than nine for the Alberta highway network). In addition to comparing the two methods, the results of additional experiments studying the characteristics of the genetic approach are included.
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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