Guidelines for Selecting Sign Sheeting to Meet Minimum Retroreflectivity Levels (Poster)
Bibliographic record
Abstract
Guidelines for Selecting Sign Sheeting to Meet Minimum Retroreflectivity Levels have been developed to assist transportation agencies in selecting sign sheeting types and maintenance strategies to provide sufficient levels of retroreflectivity for night-time drivers. The following trends have recently emerged that prompted review of sign sheeting requirements: a significant proportion of crashes occur during night-time conditions, in spite of lower traffic volumes during these times; the driving population is aging and older drivers have significantly reduced visual acuity during night-time conditions; vehicle headlight design has changed and most headlights are currently configured to aim more downward and away from signs; consequently, a lower proportion of the light is returned to the driver's eye; sign sheeting manufacturers are developing new products with increased retroreflective qualities, and; the Federal Highway Administration (FHWA) has recently conducted extensive research on minimum reflectivity requirements. The document provides guidance for minimum levels of sign retroreflectivity adopted for the Manual of Uniform Traffic Control Devices for Canada. This report was also translated to French and is available as Lignes directrices pour la selection d'une pellicule conforme aux seuils de retroreflechissance des panneaux routiers.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.061 |
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".