River dolphins (Inia geoffrensis and Sotalia fluviatilis) in the Peruvian Amazon: habitat preferences and feeding behavior
Bibliographic record
Abstract
To estimate river dolphin habitat preference through density, as well as which habitats were preferred for feeding in the Pacaya- Samiria National Reserve, surveys were conducted during the high- to low-water season transition, from 2016 to 2018, in the channels, lakes, and confluences of the Samiria River. Both the Amazon river dolphin and tucuxi dolphin showed a preference for the confluences. The wide channel (Amazon: 24.8 dolphins/ km2, tucuxi: 7.6 dolphins/km2) and narrow channel (Amazon: 73.0 dolphins/km2; tucuxi: 6.0 dolphins/km2) also had high dolphin densities, especially for the Amazon river dolphins. In contrast with previous studies, the lakes had the lowest densities of dolphins for both species. High proportions of feeding behavior were observed in the confluence and wide channel habitats. The potentially larger presence of fish in these two habitats is likely the primary reason for the high dolphin densities. The high dolphin densities in the narrow channel, on the other hand, were associated with a low proportion of feeding behavior. Therefore, there are likely separate environmental factors attracting the dolphins, although additional data will be required to determine these factors. The results of this study will continue to help identify potential conservation and management actions by contributing to a better understanding of the ecology of river dolphins and their dependence on various habitats in one of the world’s largest protected flooded forests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".