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Record W2917140794 · doi:10.4000/vertigo.21718

Concevoir la synergie des cycles pour promouvoir un métabolisme agri-urbain écologiquement efficient et réduire l’exposition humaine aux polluants

2018· article· fr· W2917140794 on OpenAlexvenueno aff
Camille Dumat, Antoine Pierart

Bibliographic record

VenueVertigO · 2018
Typearticle
Languagefr
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
Fundersnot available
KeywordsForestryChemistryHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

À l’échelle globale, les villes concentrent la majorité de l’humanité et à la façon des installations classées pour la protection de l’environnement (règlement ICPE, France) elles visent la durabilité des pratiques afin de concilier activités anthropiques, enjeux Environnement-Santé et bien-être des populations. Dans ce contexte, concevoir durablement la dématérialisation et l’économie circulaire en zones (péri)urbaines, grâce à une approche volontairement interdisciplinaire et dé-compartimentée est un objectif crucial en termes de réduction des risques environnement-santé et des inégalités écologiques. La gestion durable des polluants persistants métalliques dans les projets d’agricultures urbaines apparait ainsi comme une opportunité en faveur de la transition écologique : vulgarisation scientifique, concertations citoyennes, altérité et alimentation durable sont en effet directement impliquées. Des résultats de recherche concernant quatre éléments métalliques couramment observés dans les écosystèmes urbains : le plomb, le cadmium, le cuivre et l’antimoine, sont discutés dans le contexte de projets d’agricultures urbaines pour illustrer ces réflexions indispensables pour concevoir la synergie des cycles biogéochimiques et de vie des produits, et promouvoir un métabolisme urbain écologiquement efficient afin de réduire l’exposition humaine aux polluants.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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Same venueVertigOSame topicLand Use and Ecosystem ServicesFrench-language works237,207