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Record W7018338319

Des cancérogènes en milieu de travail? Posez-vous des questions!

2013· article· fr· W7018338319 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsDerogationContext (archaeology)Term (time)
DOInot available

Abstract

fetched live from OpenAlex

Le risque de développer un cancer associé au travail est beaucoup moins visible que celui de faire une chute ou de se blesser.Pourtant, selon la CSST, 68 décès sont survenus au Québec à la suite d'un accident du travail en 2011, contre 100 causés par un cancer lié au travail.Ce feuillet concerne tous les milieux de travail, car il n'y a pas que l'amiante qui soit cancérogène!Les cancérogènes On peut être exposé à des cancérogènes sous forme de poussières, de liquide, de gaz, d'ondes, ou autres.Ils sont parfois incolores, sans odeur et invisibles.Le tableau suivant en présente quelques exemples.Pour consulter une liste des cancérogènes, visitez le site Web du Centre international de recherche sur le cancer (CIRC) : http://monographs.iarc.fr/FR/Classification/index.phpPour connaître les cancérogènes du Règlement sur la santé et la sécurité du travail, voir son Annexe I.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.006
Scholarly communication0.0080.011
Open science0.0010.003
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0370.012

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 head0.029
GPT teacher head0.348
Teacher spread0.319 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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