Estimating fish abundance from acoustic surveys: calculating variance due to acoustic backscatter and length distribution error
Bibliographic record
Abstract
Estimation of fish abundance from acoustic surveys requires the estimation of total acoustic backscatter of the target species in the sampled region. Although the arithmetic mean of acoustic backscatter is an unbiased estimator of the mean backscatter for regular or random sampling designs, under the presence of spatial structure, its use leads to a loss of information and the estimation of its variance is not trivial. Here, we tackle these shortcomings by building a spatial model of acoustic backscatter using spline-based generalized additive models (GAMs). GAMs were used to provide local and global estimates of acoustic backscatter, and their precision was calculated by statistical simulations of the models’ parameters. For a series of surveys performed off the western and southern Iberian Peninsula, GAM estimates were unbiased and more precise than the arithmetic mean estimates. Simulations of the acoustic backscatter fields were combined with resampling of the trawls to provide confidence intervals for fish numbers and biomass. The relative standard errors of the estimates were within 13% and 46% (average 22%) for numbers and within 12% and 35% (average 19%) for biomass. Acoustic sampling error was the major contributor to the overall variance.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".