Reducing bias in Coregonus artedi abundance estimates using stationary up-looking acoustics
Bibliographic record
Abstract
Mobile hydroacoustic surveys using ship-based down-looking transducers are widely used to estimate densities for ecologically and economically important pelagic fishes. However, this method likely underestimates densities of some surface-oriented species due to biases associated with the acoustic surface exclusion zone and ship avoidance behaviours. We compared cisco ( Coregonus artedi ) density estimates from a stationary up-looking platform survey to a standard down-looking acoustic survey. Both systems were deployed during the fall cisco spawn in Thunder Bay, Lake Superior 2020–2022. Cisco density estimates from the stationary up-looking platform were on average 6.7 times higher in the upper water column (∼1–10 m) and 2 times higher over the entire water column (∼1–45 m) than those from standard mobile surveys. Ship avoidance behaviour associated with mobile surveys was apparent in the upper water column; median cisco densities observed by the platform fell from ∼36 to ∼9 fish/ha when the ship passed near the platform. Abundance estimates from the platform when not influenced by ship avoidance provided higher quota estimates than the standard survey in 2020 and 2022, but were similar in 2021. A multi-day deployment of the platform tracked a progressive daily increase in fish densities, highlighting the sensitivity of mobile survey results to the day they are conducted, often dictated by environmental conditions. Our results show promise in applying stationary acoustic deployments in fisheries surveys, with improved accuracy and reduced effort compared to mobile acoustic surveys in the management and monitoring of pelagic fishes in the Great Lakes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".