Changes in Fish Catch Rates in the Presence of Air Gun Sounds in Prudhoe Bay, Alaska
Bibliographic record
Abstract
Air guns used in oil industry seismic surveys have the capacity to change fish catch rates, but no previous work has demonstrated this effect in shallow water or in Arctic oilfields. Long-term monitoring of fish catches using four fyke nets allowed assessment of changes in catch rates during a 2014 seismic survey in Prudhoe Bay, Alaska. Fyke net locations were instrumented with both conventional hydrophones and vector sensors. Catch rates were generally within the range of those found in 27 previous sampling seasons. The effect of air guns on eight species was assessed using a modified Before-After/Control-Impact analysis, with historical data and 2014 data as the Before-After components of the analysis and days without and with air gun activity as the Control-Impact components. Results showed significant changes associated with air guns in catch rates at one or more nets at p < 0.1 for all eight species and at p < 0.05 for seven of the eight. Changes included both increased and decreased catch rates, perhaps reflecting displacement of fish in response to air gun sounds throughout the study area. Measured sound pressure levels associated with air gun pulses were low and usually undetectable close to the fyke nets, reflecting the loss of low frequencies in shallow water (~1.5 m). Attempts to measure particle velocities failed when wind-driven surface waves overwhelmed vector sensors. However, fish responses may have been related to changes in particle motion associated with air gun sounds.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".