Vocal behavior of the endangered splendid toadfish and potential masking by anthropogenic noise
Bibliographic record
Abstract
Abstract Vessel‐related noise is a potential stressor for coral reef fauna. The Parque Nacional Arrecifes de Cozumel (PNAC) is a Mexican Marine Protected Area that is exposed to pervasive vessel traffic. PNAC is also the primary range of splendid toadfish ( Sanopus splendidus, family Batrachoididae), an IUCN red‐listed soniferous fish for which vessel noise may represent a threat. We conducted a passive acoustic monitoring survey during summer of 2017 at Paraiso Reef in PNAC and obtained the first scientific recordings from splendid toadfish, enabling a vocal characterization of the species. We simultaneously collected data on sound levels of vessels passing near the reef. High noise levels of cruise ship and small motorboat traffic caused elevated anthropogenic sound pressure levels for up to 15 hr per day in the same bandwidth as toadfish vocalizations. A single cruise ship added up to 4 dB above nighttime ambient levels while small motorboat traffic added up to 7 dB. The overlap of toadfish vocalizations and vessel‐related noise highlights the susceptibility of splendid toadfish to acoustic masking and reduction in communication space throughout the day, warranting further study. Because acoustic communication is critical to toadfish reproductive success, noise from cruise ships and small motorboats may threaten splendid toadfish individuals or population viability.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".