A Canadian First Nations radon assessment and COVID-19 restrictions: A difficult pairing
Bibliographic record
Abstract
Radon is a known carcinogen and a by-product of degrading naturally occurring radioactive elements. The North Shore Micmac District Council (NSMDC) board of directors, in Eastern New Brunswick, Canada, were aware of this issue and saw a need for increased radon testing and awareness in their communities. The initial plan was to administer a testing blitz across communities to gauge the current levels of radon exposure in both residential and band-owned structures. This, with Elder consultation and a participant health survey, would create a data set used to guide future strategies effectively and better direct resources to mitigate the leading cause of lung cancer in non-smokers. These plans were put in place prior to the COVID-19 pandemic that began in March 2020. The subsequent provincial levels of restriction could not have been predicted. The ever-changing pandemic-related restrictions, and public health’s focus on a new deadly pathogen, led to difficulties managing and following through on many health and wellness projects. These circumstances led to a unique situation that delayed results, prolonged exposure to a known carcinogen, and may have consequences in the long term. Few procedures, treatments, or medications do not have side effects, and even warranted pandemic-related measures affect other aspects of health.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".