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Record W2169012688 · doi:10.4000/vertigo.12826

Pollution atmosphérique en milieu urbain : de sa régulation à sa surveillance

2013· article· fr· W2169012688 on OpenAlexvenueno aff
Laurence Lestel

Bibliographic record

VenueVertigO · 2013
Typearticle
Languagefr
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesForestryGeographyArt

Abstract

fetched live from OpenAlex

Au début du XIXe siècle, les pollutions sont visibles et industrielles. Malgré l’effort de mesures de la qualité de l’air intérieur, il n’y a eu alors ni mesure ni même définition de grilles de qualité pour l’air extérieur. Les réponses sont techniques (essai de diminution de l’émission de fumées) et réglementaires. Elles ne prennent pas en compte les pollutions d’autres secteurs d’activités (usages urbains, transports) dues à l’accroissement de l’usage du charbon. En France, les premières analyses systématiques de l’air atmosphérique sont celles de l’Observatoire de Montsouris à partir de 1876. Petit à petit, la réglementation incorpore des limites de rejets, d’abord pour les fumées noires industrielles (1934 dans le département de la Seine) puis pour les rejets des véhicules automobiles (décrets nationaux de 1969). Les épisodes de hausse de mortalité résultant de la combinaison d’événements météorologiques particuliers et d’émissions de gaz conduisent à la mise en place de réseaux de surveillance, en 1954 à Paris grâce aux efforts du Laboratoire d’hygiène de la ville de Paris, et du Laboratoire central de la Préfecture de Police, puis dans l’ensemble des grandes villes et zones industrielles de France à partir de 1973. L’organisation de ces réseaux de surveillance a été renforcée par la loi sur l’air de 1996.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0060.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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Same venueVertigOSame topicAtmospheric chemistry and aerosolsFrench-language works237,207