Hydroacoustic fish stock assessment in the presence of dense aggregations of <i>Chaoborus</i> larvae
Bibliographic record
Abstract
A new method for eliminating reverberation due to Chaoborus larvae from hydroacoustic recordings is presented based on an assumption of a constant dependence between the area backscattering strength (sa) with a high-volume backscattering threshold (sv) and sa with a low sv threshold for fish. The idea was to analyze data with a threshold high enough to eliminate reverberation and then convert the estimate to coincide with the result that would have been achieved with a low threshold containing all backscattering from fish. The approach was validated with a secondary dataset, and the magnitude of overestimation of fish density by reverberation was evaluated using data from four surveys conducted in a clay-turbid lake, where small planktivorous fish, smelt (Osmerus eperlanus), and larvae of Chaoborus flavicans coexist in the water column. With the presented method, estimation of smelt density was possible even when Chaoborus density was >200 individuals·m3. The analyses revealed that the overestimation of fish density could be as high as 50% if the reverberation is not taken into account. The presented method might also be applicable for eliminating reverberation due to other unwanted targets, because it is based on the acoustic properties of fish rather than those of unwanted targets.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".