Trace element accumulation in different edible fish species from the Bolivian Amazon and the risk for human consumption
Bibliographic record
Abstract
Artisanal mining and erosion of metalbearing soils can contaminate aquatic ecosystems and affect the health of riparian human populations, through metal bio-accumulation processes and fish consumption. Concentrations of eight trace metals (Cd, Cr, Co, Cu, Pb, Hg, Ni, Zn) and a metalloid (As) were measured in the muscle tissue of different edible fish species collected from markets of two cities along the Beni River banks, in the Bolivian Amazon. Relationships between the size of fish belonging to different trophic levels (carnivores, omnivores, detritivores and herbivorous) from four different fishing zones were analyzed. The most relevant results corresponded to the detritivore group, whose members exhibited significant positive correlations between the fish size and the concentration of three metals (cadmium, cobalt and nickel). Furthermore, a 3 × 3 scenario-risk analysis was performed to assess local risk for human health. This was done by relating three different scenarios of local fish consumption collected from literature (maximum, average and minimum) and three different levels of trace element concentrations (95 th , 50 th and 5 th percentile) derived from the present study and the Minimal Risk Levels suggested by the Agency for Toxic Substances and Disease Registry. Results of these calculations determined the amount of fish muscle per contaminant that could be consumed per day without risking human's health. Finally, Target Hazard Quotients were calculated for each trace element, aiming to indicate the potential exposure to each one and the concentration at which no adverse effects are expected. The obtained results made clear that mercury is the only trace element that represents an important health risk to humans within the studied region, considering most of the combinations in the 3 × 3 analysis. Chronic mercury intoxication could occur when consumption of fish in Riberalta or Rurrenabaque exceeded 83 g/day in females and 110 g/day in males.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".