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Research and Monitoring Efforts on First Nations Environmental Health Issues

2018· article· en· W2991569897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueISEE Conference Abstracts · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsEnvironmental healthEnvironmental monitoringEnvironmental planningEnvironmental resource managementEnvironmental protectionBusinessPolitical scienceMedicineGeographyEnvironmental science

Abstract

fetched live from OpenAlex

The First Nations and Inuit Health Branch, Health Canada/Indigenous Services Canada has been working on several large programs to assist First Nations in understanding and reducing the impact of exposure to chemical hazards in their environment: the First Nations Environmental Contaminants Program (FNECP); the First Nations Food, Nutrition and Environment Study (FNFNES) and the First Nations Biomonitoring Initiative.Initially, the Mercury Biomonitoring Program, which ran from 1970 to 2000, monitored exposure to mercury by collecting over 70,000 blood and hair samples in over 500 First Nation.The FNECP, created in 1999, supports community-based monitoring, research and risk assessment. Since 2000, the national FNECP has funded 103 national projects. Through dietary surveys and chemical exposure assessments and/or human biomonitoring, First Nations collaborated with researchers to gain important information on the chemical safety of their traditional diet. As appropriate, recommendations were made with respect to traditional food consumption.The FNFNES was created in 2008 to fill knowledge gaps on the diet and safety of traditional foods for First Nations living on-reserve south of the 60th parallel. This study was implemented region by region from 2008 to 2018. The FNFNES included five components: household interviews; drinking water sampling for trace metals; hair sampling for mercury; surface water sampling for pharmaceuticals and traditional food sampling for chemical contaminant levels.Results from the FNFNES mercury in hair sampling program will be compared to the findings of the earlier methylmercury biomonitoring program.Results from the FNFNES pharmaceutical sampling results will be explained in relation to how mixtures of pharmaceuticals can be characterized with respect to both their ecological and human health risks.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.166
GPT teacher head0.475
Teacher spread0.309 · 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