Vibrational and acoustic communication in fishes: The overlooked overlap between the underwater vibroscape and soundscape
Bibliographic record
Abstract
Substrate-borne communication via mechanical waves is widespread throughout the animal kingdom but has not been intensively studied in fishes. Families such as the salmonids and sculpins have been documented to produce vibratory signals. However, it is likely that fish taxa on or close to the substrate that produce acoustic signals will also have a vibratory component to their signal due to their proximity to substrates and energy transfer between media. Fishes present an intriguing opportunity to study vibrational communication, particularly in the context of signal production and detection, detection range, and how vibratory signals may complement or replace acoustic signals. It is highly likely that the vibrational landscape, the vibroscape, is an important component of their sensory world, which certainly includes and overlaps with the soundscape. With the wide range of anthropogenic activities modifying underwater substrates, vibrational noise presents similar risks as acoustic noise pollution for fishes that depend on vibrational communication. However, in order to understand vibrational noise, more empirical studies are required to investigate the role of vibrations in the fish environment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".