Responses to sound in three Centrarchid species: Do heterospecific interactions change behavior?
Bibliographic record
Abstract
Due to the increasing prevalence and variety of underwater anthropogenic noise sources, and the growing human population, anthropogenic noise has the potential to negatively impact aquatic organisms. With this growing threat, the question of how fishes respond to this stressor in their natural environments becomes more urgent. The current study used behavioral trials with bluegill sunfish Lepomis macrochirus, pumpkinseed sunfish Lepomis gibbosus, and rock bass Ambloplites rupestris, both in isolation and in a heterospecific trial, to determine how behaviors indicative of stress were influenced by interspecific interactions when exposed to recordings of pure tones and boat motors. Regardless of social context, all three species experienced an increase in fin beats per second, an increase in time spent at the bottom of the pen, and a decrease in time spent swimming when exposed to boat noise. Fishes in heterospecific trials experienced more fin beats per second and spent less time swimming, but there was no significant difference when comparing time spent at the bottom of pen with fish in individual trials. Our findings of behavioral changes when exposed to acoustic stimuli, in two social contexts, allow for a deeper understanding of interspecific effects and provide insight into how varied field studies can be useful in studying fish behavior when encountering acoustic stressors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".