Introduction to the special issue on fish bioacoustics: Hearing and sound communication
Bibliographic record
Abstract
Fish bioacoustics, or the study of fish hearing, sound production, and acoustic communication, was discussed as early as Aristotle. However, questions about how fishes hear were not really addressed until the early 20th century. Work on fish bioacoustics grew after World War II and considerably in the 21st century since investigators, regulators, and others realized that anthropogenic (human-generated sounds), which had primarily been of interest to workers on marine mammals, was likely to have a major impact on fishes (as well as on aquatic invertebrates). Moreover, passive acoustic monitoring of fishes, recording fish sounds in the field, has blossomed as a noninvasive technique for sampling abundance, distribution, and reproduction of various sonic fishes. The field is vital since fishes and aquatic invertebrates make up a major portion of the protein eaten by a signification portion of humans. To help better understand fish bioacoustics and engage it with issues of anthropogenic sound, this special issue of The Journal of the Acoustical Society of America (JASA) brings together papers that explore the breadth of the topic, from a historical perspective to the latest findings on the impact of anthropogenic sounds on fishes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.027 |
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".