IMPROVING WORK ZONE SAFETY THROUGH ENHANCED TEMPORARY CONDITIONS
Bibliographic record
Abstract
One of the greatest operational challenges for road authorities and contractors is keeping an existing, busy highway corridor open to traffic during maintenance, rehabilitation, reconstruction, or expansion activities. Most recently, the application of human factors knowledge and positive guidance technique to work zone safety and efficiency issues has highlighted the substantial benefits of providing adequate advance warning and directional guidance to drivers approaching work zones. Experience has shown that commuter routes benefit substantially from advance notification. On Ontario's provincial highway network, an enhanced, adaptive system of temporary conditions signing, encompassing advance notification, advance warning and alternative route information, has traditionally been employed in conjunction with large, long-duration or otherwise intrusive projects. Under this scheme, the provision of temporary conditions traffic management (TCTM) information has grown from simple Construction Ahead signing to a complex system of static and dynamic messaging on the affected roadway, and on intersecting roads and parallel routes, complimented by media advisories, toll-free and Internet road information services, and up-to-the-minute traffic reports by media outlets. In the late 1990's a TCTM Manual was commissioned. Initial experiences amongst the consultants, contractors and ministry staff with the Manual have resulted in a number of Lessons Learned, which are outlined in this paper.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".