Documenting Complex Air Pollution Mixtures and Baseline Health Conditions in Fort McKay, Alberta
Bibliographic record
Abstract
Environmental pollution from the oil sands industry in northern Alberta, Canada, is an ongoing concern. Fort McKay is an Indigenous community in close proximity to these sources. To improve understanding of the impacts of the air emissions on the environment and the community, detailed air monitoring was initiated by Environment and Climate Change Canada in 2013. Subsequent to this, but independent from it, Fort McKay joined a national Indigenous cohort which is sub-study of the Canadian Alliance for Health Hearts and Minds (CAHHM). In total 104 participants aged 18 to 69 years residing in Fort McKay were recruited, providing individual information via multiple questionnaires (medical history, risk factors, diet, physical activity), and via physical and cognitive measures and blood samples (hemoglobin A1C and Apolipoproteins). Consistent with the CAHHM national protocol, a majority (N=87) of the Fort McKay participants underwent Magnetic Resonance Imaging (brain, heart, carotid and abdomen), consented to follow-up via health record linkage and to blood storage for possible genetic research with further consent. Community-level environmental contextual factors were also assessed. Establishing this strong baseline of health data and community engagement within the broader context of a national study is expected to enable research on a range of health questions geared towards a better understanding of why Indigenous people have higher rates of death from CVD compared to the Canadian population, and how, through community-based approaches, this burden can be reduced. Potential health impacts of long-term air pollutant exposures can also be explored given the national CAHHM platform. The purpose of this presentation will be to provide an overview of this work to date, including community air pollutant exposures, challenges and opportunities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".