Sounds from seismic air guns: gear- and species-specific effects on catch rates and fish distribution
Bibliographic record
Abstract
Previous studies found that sounds generated by seismic air guns led to pronounced reductions in commercial catches from trawls and longlines and the displacement of fish from fishing grounds. In contradiction to these findings and fishermen’s concerns, we demonstrate here that gillnet catches were doubled for redfish ( Sebastes norvegicus ) and Greenland halibut ( Reinhardtius hippoglossoides ) during seismic shooting on a Norwegian fishing ground (86% and 132% increase, respectively, compared to preshooting levels). However, longline catch rates fell (16% for Greenland halibut, 25% for haddock ( Melanogrammus aeglefinus )). These contradictory results were explained by greater swimming activity versus lowered food search behaviour in fish exposed to air-gun sound emissions. Changes in catch rates of all species studied (including saithe (i.e., pollock, Pollachius virens ) and ling ( Molva molva )) were found, indicating that these species all responded to air-gun sounds. Except for saithe, acoustic mapping of fish abundance did not suggest displacement from fishing grounds. Less intense sound exposure compared with previous studies and strong habitat preference in some species may explain this finding.
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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".