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Record W7024154702

Revue de littérature sur les interactions conducteurs – usagers vulnérables

2025· other· fr· W7024154702 on OpenAlexaboutno aff

Bibliographic record

VenueEspaceINRS (National Institute for Scientific Research (Canada)) · 2025
Typeother
Languagefr
FieldComputer Science
TopicHandwritten Text Recognition Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSugar industryUrban environmentLimiting
DOInot available

Abstract

fetched live from OpenAlex

Dans le cadre de sa Stratégie de prévention en sécurité routière, la Société de l’assurance automobile du Québec (SAAQ) s’est dotée d’un plan d’action sur le partage de la route afin d’améliorer le bilan routier et la sécurité des usagers vulnérables. Dans cette stratégie, les « usagers vulnérables » réfèrent aux piétons, aux cyclistes, et aux motocyclistes. Contrairement aux automobilistes protégés par l’habitacle de leur véhicule, ces usagers de la route n’ont aucune protection, ce qui les rend particulièrement vulnérables lors de collision. La SAAQ rapporte un bilan routier avec des proportions de décès et de blessés en augmentation. En 2006-2008, la proportion de décès et de blessés graves impliquant des usagers vulnérables représentait 28,5% alors qu’en 2016-2018, cette proportion s’élève à 36,5%. Dans ce contexte, la SAAQ a donné un mandat de recherche afin de réaliser une revue de littérature sur les études traitant des situations d’interactions entre les conducteurs (de véhicules de promenade ou de véhicules lourds) et les usagers vulnérables. Marie-Soleil Cloutier, directrice du Laboratoire piéton et espace urbain (LAPS) et Nicolas Saunier de Polytechnique Montréal, proposent ici un projet de revue systématique pour répondre aux besoins exprimés par la SAAQ.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.073
GPT teacher head0.334
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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