Paired comparisons with quiet surface drones show evidence of fish behavioral response to motorized vessels during acoustic surveys in Lake Superior
Bibliographic record
Abstract
Acoustic surveys are important for fish stock assessments, but fish responses to survey vessels can bias acoustic estimates. We leveraged quiet uncrewed surface vessels (USVs) to characterize potential bias in acoustic surveys. Five conventional motorized ships overtook USVs from astern over 2 km transects at night in Lake Superior in 2022. We examined the difference in acoustic backscatter, average target depth, and average target strength (TS) between USV and motorized vessels. Although sound level measurements from the motorized vessels sometimes exceeded recommendations for scientific vessels, we did not detect differences in acoustic measures among survey vessels. However, the USVs recorded 2 dB higher acoustic backscatter and TS than motorized vessels, leading to ∼15% higher fish densities with drones when using in situ TS and echo integration. Differences in fish density would increase to 30%–60% if a standard TS value was applied. Target depth did not differ between USVs and motorized ships. These results are consistent with a change in orientation but not depth of insonified fish and limited horizontal avoidance of motorized survey vessels.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".