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Record W3197116048 · doi:10.3917/eg.493.0250

Exposition des cyclistes à la pollution sonore et atmosphérique à Lyon, France

2021· article· fr· W3197116048 on OpenAlexaff
Philippe Apparicio, Jérémy Gelb, Vincent Jarry, Élaine Lesage‐Mann, Sophie Debax

Bibliographic record

VenueL’Espace géographique · 2021
Typearticle
Languagefr
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

L’objectif de cet article est de modéliser l’exposition des cyclistes au bruit et au dioxyde d’azote (NO 2 ) à Lyon (France). Les données primaires (1 095 km), collectées à vélo en février 2019, permettent de construire trois modèles bayésiens (modèles généralisés additifs à effets mixtes avec un terme autorégressif) avec, comme variables dépendantes : le bruit (dB(A)), la concentration de NO 2 (μg/m 3 ) et l’inhalation de NO 2 (μg). Les résultats montrent que les expositions des cyclistes et l’inhalation de NO 2 varient significativement en fonction des types d’axes qu’ils empruntent. Par conséquent, ces deux nuisances urbaines devraient être prises en compte lors de la planification des voies cyclables.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.293
Teacher spread0.272 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2021
Admission routes1
Has abstractyes

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