Freshwater soundscapes: a cacophony of undescribed biological sounds now threatened by anthropogenic noise
Bibliographic record
Abstract
Abstract The soundscape composition of freshwater habitats is poorly understood. Our goal was to document the occurrence of biological sounds in a large variety of freshwater habitats over a large geographic area. The underwater soundscape was sampled in freshwater habitat categorized as brook/creek, pond/lake, or river, from five major river systems in North America (Connecticut, Kennebec, Merrimack, Presumpscot, and Saco) over a five-week period in the spring of 2008. Over 7,000 sounds were measured from 2,750 minutes of recording in 173 locations, and classified into major anthropophony (airplane, boat, traffic, train and other noise) and biophony (fish air movement, also known as air passage, other fish, insect-like, bird, and other biological) sound categories. Anthropogenic noise dominated the soundscape of all habitats averaging 15 % of time per recording compared to less than 2 % for biological sounds. Anthropophony occurred in 79 % of recordings and was mainly due to traffic and boat sounds, which exhibited significant differences among habitats and between non-tidal and tidal river regions. Most biophonic sounds were from unidentified insect-like, air movement fish, and other fish sound sources that occurred in 57 % of recordings. Mean frequencies of anthropogenic noises overlapped strongly with the biophony, and comparisons of spectra suggest that insect- like and air movement sounds may be more susceptible to masking than other fish sounds. There was a significant decline in biodiversity and biophony with increasing ambient sound levels. Our poor understanding of the biophony of freshwater ecosystems, together with an apparent high temporal exposure to anthropogenic noise across all habitats, suggest a critical need for studies aimed at identification of biophonic sound sources and assessment of potential threats from anthropogenic noises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".