Soundscape Maps of Soniferous Fishes Observed From a Mobile Glider
Bibliographic record
Abstract
Most passive acoustic studies of the soundscape rely on fixed recorders, which provide good temporal resolution of variation in the soundscape, but poor spatial coverage. In contrast, a mobile recording device can show variation in the soundscape over large spatial areas. We used a Liquid Robotics SV2 wave glider fitted with a tow body with a passive acoustic recorder and hydrophone, to survey and record the soundscape of the Atlantic Ocean off North Carolina (United States). Recordings were analyzed using power spectral band (PSB) sums in frequencies associated with soniferous fish species in the families Sciaenidae (drums and croakers), Ophidiidae (cusk-eels), Batrachoididae (toadfish), Triglidae (sea robins), and Serranidae (groupers). PSB sums were plotted as the wave glider moved offshore and along the coast, came back inshore, and circled artificial and natural reefs. The soundscape in water <20 m was dominated by nocturnal fish choruses with PSB sums > 120 dB re 1 μPa 2 : a Sciaenidae mixed-species chorus, an unknown “grunt” chorus, an unknown “buzz” chorus, and an Ophidiidae chorus. The Ophidiidae and unknown “buzz” fish choruses dominated in the range of 1600–3200 Hz and were similar in sound pressure level (SPL) to the US Navy recordings made at Cape Lookout (136 dB in 2017 vs. 131 dB in 1943). In deeper water (27–30 m), we recorded Triglidae “honks,” oyster toadfish “boat whistles,” Sciaenidae “booms” and “clucks,” and grouper “growls.” We recorded a nocturnal 5600–Hz signal while the glider was passing near known live bottom reefs and artificial reefs. Vessel noise (100–200 Hz) was part of the soundscape in shipping lanes as large cargo vessels passed by the glider. Rainfall and thunder were also part of the soundscape. The maximum SPL observed (148 dB re 1 μPa) occurred during a mixed-species Sciaenidae fish chorus near Cape Lookout that was dominated by unknown “grunt” calls. Passive acoustic monitoring from mobile platforms can be used to discover and map the locations of fish choruses, identify areas of their habitat use, and locate previously unknown locations of reefs and fish spawning areas during ocean surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".