On-road and Laboratory Evaluation of Bilingual Variable Message Signs
Bibliographic record
Abstract
The purpose of this study was to accommodate bilingual (English/French) messages on freeway variable message signs (VMS), while maintaining sign effectiveness. The current English-only 3 line (3L) messages were compared with bilingual 3L messages (using symbols and text) and bilingual 4L messages (text only, 2 lines per language). A calibration study, including on-road and laboratory testing, was used to establish appropriate presentation times for VMS messages in a laboratory study. There was no significant difference in performance between Anglophones and Francophones for the 4L signs or for the English only signs. However, Francophones performed significantly worse than Anglophones on the 3L signs, likely due to lack of separation of the languages. For both Anglophones and Francophones, using colour (to indicate degrees of congestion), on the 3L sign helped improve performance more than adding it on the 4L sign (where it was used to differentiate English (white) and French (yellow), though the effect was a very weak trend. Overall, for both language groups, the 4L sign was preferred.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".