MétaCan
Menu
Back to cohort
Record W2555247184 · doi:10.1684/ers.2016.0886

Faire d’une pierre deux coups : retombées positives d’actions contre les îlots de chaleur urbains

2016· article· fr· W2555247184 on OpenAlexaboutno aff
Mélanie Beaudoin

Bibliographic record

VenueEnvironnement Risques & Sante · 2016
Typearticle
Languagefr
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Pour contrer les ilots de chaleur urbains (ICU), l’Institut national de sante publique du Quebec (INSPQ) a mis en place, en 2009, un programme de subventions. L’objectif de ce programme etait d’ameliorer la sante de la population par le biais de projets de demonstration de mesures efficaces de lutte contre les ICU realises dans plusieurs regions du Quebec. Plus de quarante projets ont ete ainsi finances, visant principalement la vegetalisation d’espaces betonnes et asphaltes. Une evaluation de la qualite de vie des citoyens a ete realisee par le biais de sondages et de questionnaires, dans l’annee qui a suivi la realisation de certains des projets. S’il etait trop tot pour qu’ils percoivent un benefice important en ce qui concerne la baisse de la temperature, puisque les vegetaux n’etaient pas a maturite, l’evaluation a tout de meme montre leur satisfaction des projets realises. Plus encore, des entretiens avec les porteurs de projets ont etabli plusieurs retombees positives sur les communautes, notamment quant a la cohesion sociale, la securite et l’adoption d’habitudes de vie plus saines.

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)0.025
metaresearch head score (Gemma)0.035
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 score0.200
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.006
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0150.003

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 head0.258
Teacher spread0.238 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEnvironnement Risques & SanteSame topicUrban Green Space and HealthFrench-language works237,207