Fish Sounds and Noise Pollution: Engaging the Public in Bioacoustics
Bibliographic record
Abstract
Science literacy is essential to ensure public awareness of research findings and foster meaningful engagement with scientific endeavors. However, a disconnect persists between scientific research and public understanding, as outreach is frequently overlooked in favor of data collection, analysis, and publication. Here, the authors describe their outreach initiative, FishSounds Educate, aimed at increasing the accessibility of ocean acoustics research, with a focus on fish sounds, noise pollution, and the broader effects of sounds on aquatic life. Bioacoustics provides an engaging entry point into topics such as fish and invertebrate biology, marine ecology, the physics of sound, and human impacts on aquatic environments. To cultivate public engagement, the authors developed and implemented interactive workshops, activity tables, seminars, and a coloring book, adaptable for all ages, from elementary to university-level students and lifelong learners. As of summer 2025, they have reached almost 4000 participants with more than 80 visits to schools, universities, aquariums, nature clubs, and public programs. Through FishSounds Educate, the authors have gained valuable insights into making bioacoustics research more accessible and are applying these lessons to ongoing work, including adapting a recent meta-analysis for young readers. Their experiences highlight the benefits of integrating outreach into research from the outset.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".