Perception of pollution and arsenic in hair of indigenous living near a ferronickel open-pit mine (Córdoba, Colombia): Public health case repor
Bibliographic record
Abstract
Introduction: Indigenous Zenu residents living near the Cerro Matoso ferronickel mine (Montelibano, Cordoba, Colombia) have complained for many years about adverse health effects. Objective: To explore the perception of sources of pollution, adverse health effects and arsenic levels in the hair of residents near the mine in 2015. Case presentation: Two nominal grouping sessions were conducted (with men and women, separately). The skin of 15 individuals was examined for spots suggestive of hydroarsenicism. Seven hair samples were collected from women and sent to the Centre de Toxicologie du Québec for analysis with inductively coupled plasma mass spectrometry. The proximal, medium and distal segments of the hair were evaluated (n=21). The participants identified the ferronickel mine as the main source of pollution in the region. The exposure pathways they reported correspond to those recognized by environmental health for NiO and arsenic. The perceived adverse effects from the pollution are consistent with what can be expected when NiO and arsenic are present. The arsenic concentrations in hair ranged from 0.011 to 0.26 μg/g. The highest occurred roughly three years earlier in a girl who was 9 years old at that time. Conclusions: Exposure to arsenic near the ferronickel mine was confirmed, in addition to NiO, mercury and other metals. Future studies could explore the occurrence of adverse effects from arsenic, such as cancer, dermatosis, high blood pressure and reproductive and cardiovascular disorders.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".