Bibliographic record
Abstract
Depuis de nombreuses annees, le ministere des Transports du Quebec (MTQ) souhaite diminuer le nombre de collisions frontales sur son reseau routier. Il s'agit du type d'impact le plus recense lors d'une collision qui cause la mort sur les routes de la province du Quebec. Differents amenagements et mesures ont ete implantes, mais les collisions frontales constituent toujours une preoccupation pour le ministere dans la poursuite d'une amelioration globale du bilan routier. Aussi, pour diminuer le risque de collisions, plusieurs conversions de routes a deux voies contigues en autoroutes sont realisees, alors que les debits vehiculaires sont faibles. L'impact economique et environnemental de la conversion est aussi important dans un contexte de developpement durable. En Europe, un concept de route a ete developpe : il s'agit des routes de type 2+1. Cette conception geometrique s'inscrit dans un mouvement de reflexion de nouvelle facon de faire qui a debute dans les annees 1990, ou certains pays europeens avaient comme objectif la diminution importante des collisions mortelles/blessures corporelles et ce, en respectant les finances publiques des pays et le developpement durable. Les premieres experiences de l'application des routes 2+1 demontrent des gains satisfaisants au niveau de la securite et de la circulation. Pour la fiche generale du congres voir numero de controle 201111RT334E.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".