The importance of context in the acoustic behaviors of marine, subtropical fish species
Bibliographic record
Abstract
Despite the importance of acoustic signaling in fishes, the prevalence of the behavioral contexts associated with their active (i.e., intentional) sound production remains unclear. A systematized review was conducted to explore documented acoustic behaviors in marine, subtropical fishes and potential influences affecting their relative pervasiveness. Data were collected on 186 actively soniferous fish species studied across 194 publications, identified based on existing FishSounds and FishBase datasets. Disturbance was the most common behavioral context associated with active sound production-reported for 140 species or 75% of the species studied-and then aggression (n = 46 species, 25%) and reproduction (n = 34 species, 18%). This trend, however, somewhat differed when examined by research effort, study environment, and fish family, such as reproductive sounds being more commonly reported by studies conducted in the wild. The synthesis of fish sound production behaviors was in some ways stymied by the fact that many species' sound production did not have discernible associated behavioral contexts and that some investigations did not clearly identify the study environments in which active sound production was observed. These findings emphasize the importance of context-behavioral or otherwise-when studying acoustic behaviors in fishes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".