Fisheries of the scavenger species Calophysus macropterus: a case study in the Bolivian Amazon
Bibliographic record
Abstract
Bolivian Amazon small-scale fisheries traditionally focus on large migratory fish species. However, in the last decade there has been a trend of increased landings of low-value medium-sized migratory species, such as blanquillo, Calophysus macropterus. This scavenger species is captured using chicken and bovine waste as bait, and occasionally carcasses from wild species. This study explores the boom of this specialized fishery in Puerto Villarroel, a small harbor on the Ichilo River, using a combination of historical landing data, fisheries statistics obtained by participatory fisheries monitoring, fish market studies and interviews with local key stakeholders. Additionally, mercury contamination of fish meat was evaluated. Blanquillo fishing started in 2008 and boomed around 2015, representing 30% of landings, after which it decreased to fluctuate around 15%. The capture and landing data suggest that this species is mainly captured by non- organized fishers. The market studies conducted between 2015 and 2018 in Cochabamba city and in lowland middle towns showed that on average more than 20% of consumption of Amazon river fish consisted of blanquillo meat. Mercury is accumulated in the muscle tissue at concentrations near the official limit established by WHO for human consumption, whereas in 15% of fish the concentrations exceeded this limit. The paper pieces together the evolution of the blanquillo fishery in Bolivia, and looks at different aspects, including the diversity of fishers involved, possible impacts on other vulnerable species and public health, and potential impacts and effectiveness of different public policy approaches. In conclusion, the present study shows there is an overall need for more consistent and inclusive fisheries regulations in the Bolivian Amazon, as a way to protect aquatic fauna, avoid environmental contamination and secure 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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".