Geocide, Ecocide, and Genocidal Type Outcomes from Large-Scale Open Pit Mountaintop Gold Mining in the Outskirts of Paracatu, Brazil
Bibliographic record
Abstract
In 1987, large-scale open pit mountaintop gold mining was initiated in the outskirts of the Paracatu town, Minas Gerais, Brazil. The mine hard rocks contain low-grade gold ore (average 0.4 g gold/ton ore) and abnormally high amounts of arsenopyrite (FeAsS; average 1000 g/ton ore). Since 2005, the mine has been operated solely by Canadian Kinross Gold Corporation (KGC-NYSE, K-TSX) through its local subsidiary. The poorly controlled mining activities release FeAsS and its weathering products from the rocks as particulate matter, gas, and solutes that contaminate the air, soils, surface water and ground water. As of 2016, the cumulative inorganic arsenic throughput of the mine was estimated at 735,000 ton. In this article, we present the first series of sentinel cases of chronic arsenic intoxication (CAsI) in Paracatu. Despite mounting evidence for wanton widespread environmental degradation, large-scale environmental contamination, chronic mass intoxication, and persistent human rights abuse, a number of public and private lawsuits failed to halt Kinross' activities in Paracatu. The ongoing environmental and humanitarian disaster and unlawful abuses in Paracatu that degrade the environment and victimize thousands of people have prospered unchallenged and cannot be stopped locally due to officials' collusion with large economic interests, powerful political interferences, facilitation payments, willful blindness, and toxicological greenwashing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".