Towards better control of chemical risks associated with mining operations in Quebec (Canada) Part 1: Repertory of chemicals used in mining
Bibliographic record
Abstract
Abstract As of 2020, the mining industry in the province of Quebec (Canada) has been providing 16,000 jobs, the majority of these in the north part of the province and in the Abitibi-Témiscamingue region. From the exploration phase to the mining site rehabilitation phase, numerous chemical processes are used, often involving some of the most dangerous substances used on industrial scales. The hazards associated with many of these substances are in some cases not obvious, and symptoms of exposure may take years to appear. The risks associated with the industrial use of chemicals in Canada and abroad has long preoccupied health authorities, given the severity of the diseases that can occur. In Quebec, occupational diseases associated with exposure to industrial chemicals are reaching 1,500 cases per year. The principal focus of this study, presented in two articles, is to set a preliminary theoretical framework for categorizing chemicals in terms of their effects on the health of workers throughout the various phases of mining projects. The objective is to decrease (over the long term) the number of occupational diseases due to the use of chemicals by raising awareness among employers and exposed workers in the mining sector. This research was conducted in four phases. The present article contains the results of the first two stages, that is, a review of the literature to catalog the industrial chemicals used in mines and to list the potential effects of exposure to them, based on Safety Data Sheets. Eighty-five (85) chemicals used in at least one mining project phase and dozens of potential effects on worker health were identified. The rest of the study is presented in a second article [1]
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".