Reducing disease and death from Artisanal and Small-Scale Mining (ASM) - the urgent need for responsible mining in the context of growing global demand for minerals and metals for climate change mitigation
Bibliographic record
Abstract
Artisanal and small-scale mining (ASM) takes place under extreme conditions with a lack of occupational health and safety. As the demand for metals is increasing due in part to their extensive use in 'green technologies' for climate change mitigation, the negative environmental and occupational consequences of mining practices are disproportionately felt in low- and middle-income countries. The Collegium Ramazzini statement on ASM presents updated information on its neglected health hazards that include multiple toxic hazards, most notably mercury, lead, cyanide, arsenic, cadmium, and cobalt, as well as physical hazards, most notably airborne dust and noise, and the high risk of infectious diseases. These hazards affect both miners and mining communities as working and living spaces are rarely separated. The impact on children and women is often severe, including hazardous exposures during the child-bearing age and pregnancies, and the risk of child labor. We suggest strategies for the mitigation of these hazards and classify those according to primordial, primary, secondary, and tertiary prevention. Further, we identify knowledge gaps and issue recommendations for international, national, and local governments, metal purchasers, and employers are given. With this statement, the Collegium Ramazzini calls for the extension of efforts to minimize all hazards that confront ASM miners and their families.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.027 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".