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Record W2060374757 · doi:10.1080/00039890409602960

Exposure of a Cree Population Living near Mine Tailings in Northern Quebec (Canada) to Metals and Metalloids

2004· article· en· W2060374757 on OpenAlexaffabout
Dominique Bussières, Pierre Ayotte, Patrick Levallois, Éric Dewailly, Evert Nieboer, Suzanne Gingras, Suzanne Côté

Bibliographic record

VenueArchives of Environmental Health An International Journal · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsMcMaster UniversityUniversité LavalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsCadmiumPopulationMetalloidEnvironmental healthMedicineArsenicTailingsUrineSeleniumDemographyChemistryInternal medicine

Abstract

fetched live from OpenAlex

The authors investigated the effect of residues from copper- and gold-mining on the Cree population of Oujé-Bougoumou, located 560 km north of Quebec City, Canada. Subjects (225) from Oujé-Bougoumou and a control population (100) completed a questionnaire on lifestyle and dietary habits and provided blood and urine samples for analysis. Geometric means of arsenic, lead, cadmium, and copper concentrations were not significantly different for subjects or controls 15 yr and older or children (8-14 yr old). However, blood zinc was higher and selenium was lower in Oujé-Bougoumou samples. Mean blood lead level was higher in children from Oujé-Bougoumou, but lower in adults aged 40 yr and older. For adults (15 yr and older) blood lead level increased with age and was higher in men, those who hunted, and consumed wild meat (R2 = 0.43). Blood cadmium increased with age and smoking (R2 = 0.61). No influence of mine residues was observed among residents of Oujé-Bougoumou, but lifestyle exposure associations were noted for both communities.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.247
Teacher spread0.239 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueArchives of Environmental Health An International JournalSame topicHeavy Metal Exposure and ToxicityFrench-language works237,207