Abiotic and biotic factors influencing heavy metals pollution in fisheries of the Western Amazon
Bibliographic record
Abstract
The escalating industrial and artisanal extraction of natural resources in the Amazon Basin has centered it into the spotlight of global environmental concern. This study deals with the environmental problems of heavy metals pollution and biomagnification, which stem from mining, agricultural, petrochemical, and industrial discharges. We conducted an extensive investigation, analyzing 360 fish specimens representing 58 species, to assess the concentrations of 11 metals (Al, As, Cd, Cr, Cu, He, Mn, Ni, Pb, Zn, and Hg) along the Ecuadorian sections of the Napo and Pastaza watersheds. Stable isotope analyses (δ15N and δ13C) were employed to determine their trophic positions and potential biomagnification risks. Sampling was carried out during two distinct hydrological seasons to capture seasonal variations in metal concentrations. Furthermore, we estimated species-specific daily heavy metal intake levels. The results of our study revealed that 15 fish species and 53 individual specimens exhibited heavy metal concentrations surpassing recommended standards, with elevated levels of Al, As, and Hg posing significant risks to human consumers with daily intakes of 0.46 kg/day. Notably, our investigation unveiled concerning trends, including increased metal concentrations, Hg biomagnification within fish populations from the Pastaza watershed, and an association between smaller fish size and higher metal content. This study provides a crucial baseline for monitoring future environmental changes and understanding the persistent impacts of heavy metal pollution in the Ecuadorian Amazon region and the broader Western Amazon. It underscores the urgent need for sustainable environmental management practices to mitigate the escalating threat to these vital ecosystems.
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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 | 0.000 | 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".