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Record W2039792086 · doi:10.4000/vertigo.12775

Pollution d’origine routière et environnement de proximité

2013· article· fr· W2039792086 on OpenAlexvenueno aff
Philippe Branchu, Anne-Laure Badin, Béatrice Bechet, Laurent Eisenlohr, Tiphaine Le Priol, Fabienne Marseille, Elise Trielli

Bibliographic record

VenueVertigO · 2013
Typearticle
Languagefr
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

La pollution d’origine routière, liée aux émissions du moteur à l’échappement, à l’usure des véhicules, de la chaussée et des équipements de la route, constitue une pollution chronique qui affecte directement l’environnement de proximité via les eaux de ruissellement et les dépôts atmosphériques secs et humides. Les milieux impactés sont les hydrosystèmes superficiels et/ou souterrains, l’atmosphère, les sols et les végétaux qu’ils supportent. Les recherches menées depuis les années 80 ont permis d’établir un bilan environnemental de cette pollution de proximité (sources, vecteur de transfert, impact). Ces connaissances ont à leur tour permis de mettre en place des outils méthodologiques en accompagnement des politiques publiques en matière d’aménagement des infrastructures. En France, une grande partie de ce travail a été menée au sein du Réseau Scientifique et Technique de l’actuel ministère de l’Écologie (anciennement ministère de l’Équipement). Cet article constitue une illustration des travaux de recherche et méthodologiques menés et présente un point des connaissances acquises au cours de ces 30 dernières années.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2013
Admission routes1
Has abstractyes

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Same venueVertigOSame topicSmart Materials for ConstructionFrench-language works237,207