Fishers’ knowledge on abundance and trophic interactions of the freshwater fish Plagioscion squamosissimus (Perciformes: Sciaenidae) in two Amazonian rivers
Bibliographic record
Abstract
Abstract Small-scale fisheries provide income and food security to local peoples around the world. In the Brazilian Amazon, the pescada (Plagioscion squamosissimus) is among the fishes that contributes most to catches in small-scale fisheries. Our main goal was to evaluate the abundance, size, relevance to small-scale fisheries and trophic ecology of P. squamosissimus in the Tapajós and Tocantins rivers, in the Brazilian Amazon. We combined data from fishers’ local ecological knowledge (LEK) and fish sampling. We expected that fishers in the Tapajós River, less altered by anthropic changes, would cite a higher abundance, larger size and more prey and predators of P. squamosissimus. We interviewed 61 and 33 fishers and sampled fish in nine and five sites in the Tapajós and Tocantins rivers, respectively. The comparison between fishers’ citations and fish sampled indicated a higher relevance of P. squamosissimus to fishers in the Tapajós River, where this fish had an average larger size and where the fishers mentioned more food items. This pattern could be partially related to the history of anthropogenic changes in the Tocantins River. These results indicated that P. squamosissimus is a generalist fish, which could be resilient to fishing and environmental pressures.
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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 | 0.002 |
| 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 | 0.000 | 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".