Mercury content and consumption risk of the invasive Arapaima gigas (paiche) in Bolivia
Bibliographic record
Abstract
Mercury is a global pollutant present in the environment. The most frequent non-occupational human exposure pathway is fish consumption. Generally, fish species of large size and with carnivorous or omnivorous feeding habits have higher mercury concentrations. The invasive fish Arapaima (popularly known as paiche), an omnivorous large-sized species introduced in northern Bolivian Amazon about 50 years ago after escape from aquaculture in Peru, has successfully colonized this region and is now the main commercial fish species. Consumption of this species in Bolivia is increasing, and an evaluation of the risk of mercury exposure for human health is warranted. Muscle samples from 86 fish were taken from four different sub-basins (Orthon, Madre de Dios, Beni and Yata). 8.1% of the samples showed mercury content above the safe consumption recommendation of the World Health Organization (0.5 mg kg-1). Samples from the Madidi and Yata rivers scored the highest mean concentrations (0.367 and 0.306 mg kg-1, respectively), whereas individuals from the lower Beni subbasin showed the lowest (0.105 mg kg-1). According to our results, the maximum recommended paiche meat consumption is 316 g per week, divided into two meals, which is in agreement with international recommendations for fish consumption (227 g to 340 g per week), although this can vary according the place of origin of the meat. Following these recommendations, mercury exposure through paiche consumption should not represent a risk for human health.
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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 | 0.000 | 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".