Using accurate insonification volumes to estimate fish abundance from acoustic survey data
Bibliographic record
Abstract
Acoustic transect-surveys paired with biological sampling of detected fish targets are an effective method for surveying fish in large freshwater and marine basins. While echo data can be deconvoluted for detected fish targets and biomass through well-established biophysical models by echo-counting, trace-counting and echo-integration methods, commonly adopted geometric models for insonification volumes of transecting sound-beams are either inaccurate or erroneous, which can significantly bias estimated fish densities and abundances for surveyed basins. In this paper, we present accurate models for insonification volumes of acoustic beams to correct these errors. The practical value of the models is demonstrated with results from the acoustic survey data acquired in two major nursery lakes for sockeye salmon ( Oncorhynchus nerka ) on Vancouver Island in British Columbia, Canada. By using the correct ping-to-ping insonification volumes and taking considerations of unsampled volumes in the upper water column, estimates using accurate insonification volumes were within a 5 % difference between the echo-counting and trace-counting biophysical models. The presented results and methodologies can be readily replicated and are broadly applicable to acoustic surveys of fish abundance in freshwater and marine environments.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".