How Can We Understand Freshwater Soundscapes Without Fish Sound Descriptions?
Bibliographic record
Abstract
Abstract The ecological importance of the freshwater soundscape is just beginning to be recognized by society. Scientists are beginning to apply Passive Acoustic Monitoring (PAM) methods that are well established in marine systems to freshwater systems to map spatial and temporal patterns of behaviors associated with fish sounds as well as noise impacts on them. Unfortunately, these efforts are greatly hampered by a critical lack of data on the sources of sounds that make up the soundscape of freshwater habitats. A review of the literature finds that only 87 freshwater species have been reported to produce sounds in North America and Europe over the last 200 years, accounting for 5% of the known freshwater fish diversity. The problem is exacerbated by the general failure of researchers to report the detailed statistical descriptions of fish sound characteristics that are necessary to develop PAM programs. We suggest that publishers and editors should do more to encourage reporting of statistical properties of fish sounds. In addition, we call for research, academic, and government agencies to develop regional libraries of fish sounds to aid in PAM and anthropogenic noise impact studies.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".