Soundscape fishing: Spatial variability in a low-frequency fish chorus in the southern California kelp forest
Bibliographic record
Abstract
The kelp forests off the coast of southern California support a diverse assemblage of fishes, many of which are known to produce sound. Here, the spatial variability of a low-frequency (325–545 Hz) fish chorus recorded at three sites near the kelp forests off La Jolla, California, is described. This chorus dominated the dusk soundscape at all sites in May/June 2015, 2016, and 2017. During these times, spectral levels around 400 Hz increased by approximately 30 dB over a period of 3 h between 19:00 and 22:00 local time. The location of the fish chorus was estimated during each year using beamforming and time difference of arrival (TDOA) techniques on signals recorded by either a two-element 30-m aperture linear seafloor array or an array with four-elements, 20-m aperture in a tetrahedral-shaped configuration. This location was relatively constant during the chorusing each night. Environmental factors such as temperature, macroalgae assemblage and bottom cover, and geological features were investigated as possible drivers of the spatial distribution of the chorus. [Research supported by California Sea Grant (R/HCME-28) and a Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship-Doctoral (PGS D-3).]
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".