Noise elicits the Lombard effect in midshipman mating vocalizations
Bibliographic record
Abstract
Anthropogenic noise pollution is an emerging global threat to fish populations. Among a suite of deleterious effects, noise can potentially impede reproductive success in some fishes by masking their mate advertisement vocalizations. Using the plainfin midshipman fish (Porichthys notatus), a marine toadfish that produces a distinctive “hum” during courtship, we investigated how noise affects male vocalizations and spawning success in the wild. We recorded nesting males for three days and measured the frequency, amplitude, and duration of their vocalizations before, during, and after exposure to artificial noise (a c. 118-Hz tone). We also counted eggs in nests exposed to 10 days of artificial noise versus control nests that were not exposed to artificial noise. Males exposed to noise reduced the number of vocalizations they produced, reduced the frequency of their vocalizations, and increased the amplitude of their mating hum (Lombard effect). However, chronic noise exposure did not clearly affect spawning success, suggesting that the Lombard effect allowed males to sustain clear advertisement signals when competing with the relatively weak artificial noise source.
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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 | 0.000 | 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) | 0.001 | 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".