Evaluating the effect of seismic surveys on fish — the efficacy of different exposure metrics to explain disturbance
Bibliographic record
Abstract
To assess potential disturbance effects on fish from seismic air-gun surveys, we described several metrics to characterize the exposures from such surveys, including the number of emissions by area and time, and metrics based on accumulated sound exposure levels (SEL). For the SEL-based metrics we used both a simple spherical–geometrical model and a model that incorporated physical sound propagation properties such as bottom topography and the vertical difference in sound speed. We applied the metrics to two experiments in Norwegian waters (the Nordkappbanken and Vesterålen experiments) where fish distributions and fisheries were affected by the air-guns, but where the disturbance was stronger in the Nordkappbanken case. The metrics based on the number of emissions by area and time showed a stronger impact in the Nordkappbanken case. For the SEL-based metrics, the simple sound propagation model failed because of artificially elevated levels close to the emissions, but for the more complex propagation model, contrary to expectations, a stronger SEL was found in the Vesterålen case. We conclude that simple sound propagation models should be avoided and that the reliance on sound energy metrics like SEL for disturbance effects must be interpreted with caution.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".