The potential of passive acoustic monitoring for the study of ecological interactions among freshwater Amazonian dolphins and fishes
Bibliographic record
Abstract
The acoustic behavior of Amazonian aquatic fauna and the importance of its soundscape are poorly understood. Sounds produced by wild river dolphins (Amazon River dolphin, Inia geoffrensis, and tucuxi, Sotalia fluviatilis) and those of unidentified fishes were recorded from a drifting boat on six different days (8.5 h duration) in July 2012, in the Pacaya-Samiria National Reserve of Peru. Unidentified sounds of fishes were dominated by four broad types: pulsed stridulation, long stridulation, long pulse, and short pulse. Dominant sounds produced by dolphins included echolocation click trains, burst-pulses, whistles, and bubble bursts. Soniferous activity was quantified as total sound duration per 10 s of recording and compared between dolphins and fishes for each sound type and all types combined. Soniferous activity was highly variable among days, with echolocation click trains (7.7 s min-1) and pulsed stridulation (0.33 s min-1) being the dominant components. Soniferous activity of the dolphins and fishes was correlated (Spearman r = 0.49, P < 0.001). However, whether the correlation resulted from predator-prey interactions or other spatial factors could not be determined. Although preliminary in nature, this study is the first examination of the soniferous activity of both river dolphins and fishes in the Amazon and suggests passive acoustic monitoring has the potential to provide unique insight into ecological interactions in the system.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".