Which fish is this? Fishers know more than 100 fish species in megadiverse tropical rivers
Bibliographic record
Abstract
Ethnobiological studies on folk, common, or popular names that fishers use to identify fish can help improve fisheries monitoring and collaborations between fishers and researchers. This study investigates fishers’ knowledge (recognition, naming, and habitat use) on 115 and 119 fish species, respectively, in the Negro and Tapajos Rivers, two megadiverse rivers in the Brazilian Amazon, and investigates the relationship between such knowledge and fish importance to fisheries, fish abundance, and fish size. We also compared fishers’ perceptions on fisheries and fish abundance with literature data on fish harvests and fish sampling. We interviewed 16 fishers in 16 communities (one fisher per community, 8 communities along each river). These fishers recognized an average of 91 ± 10.4 species in the Negro River and 115 ± 7.2 species in the Tapajos River, but all fishers recognized 114 species in Negro and all species in Tapajos. The fishers’ knowledge of fish species was positively related to fishers’ perceptions on fish abundance, size, and importance to fisheries in the Negro, but only positively related to fish size in the Tapajos. Our results highlight the usefulness of fishers’ knowledge to providing data on use and cultural relevance of fish species in high diversity aquatic 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".