Quantifying fish avoidance of small acoustic survey vessels in boreal lakes and reservoirs
Bibliographic record
Abstract
Abstract Mobile hydroacoustic surveys are increasingly used to assess the distribution and abundance of freshwater fish; yet, fish may avoid moving vessels, potentially introducing bias in these assessments. In this study, avoidance in boreal lakes and reservoirs was quantified by developing a simple method based on paired drift:drift (D:D) and drift:motor (D:M) transects. Two systems in eastern Manitoba, Canada were studied: Lac du Bonnet reservoir and Nopiming. Acoustic data were collected using a digital DTX echosounder (BioSonics, Seattle, WA, USA), with a downward facing 200‐kHz split‐beam transducer, deployed from 5.5‐m vessels (Boston Whalers) modified for acoustic research. An avoidance coefficient ( Ac ) was developed based on comparisons of acoustic fish densities while the vessel moved over the same transects by drifting, and by motoring at survey speeds. Ac did not differ significantly from 1 (no avoidance) at Nopiming (median of 0.81, n = 13), but did at Lac du Bonnet (median of 0.51, n = 31, P < 0.05). Variability in Ac was as high in transect pairs and was unrelated to fish depth (mean 6.9 m at Lac du Bonnet; 13.1 m at Nopiming) or survey speed (up to 3.70 m·s −1 , 7 knots). Results indicated that fishes did not dive in the presence of the motoring vessel, nor was avoidance size‐based. We did not detect any evidence of fish attraction to our drifting vessel. Our results suggest that boat avoidance during acoustic surveys of shallow boreal lakes may vary in relatively similar water bodies but can be quantified experimentally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".