A multifrequency method to classify and evaluate fisheries acoustics data
Bibliographic record
Abstract
Acoustic surveys have been conducted on Georges Bank from 1998 to present to estimate Atlantic herring (Clupea harengus) population abundance. Acoustic data were collected with a 12 or 18, 38, and 120 kHz Simrad EK500 scientific echo sounder. A pelagic trawl and underwater video images were used to collect biological information and to verify the species composition of acoustic backscatter. A multifrequency classification method was developed to improve the efficiency and accuracy of classifying species from acoustic echograms. In this method, a volume backscatter (S v ) threshold was applied equivalently to all echograms, and then a composite echogram was created based on which frequencies had S v greater than or less than the S v threshold. The results of this method were compared with the standard method of visually scrutinizing regions, and metrics were developed to evaluate the accuracy of classification algorithms relative to current methods, as well as to assess the effects of classification methods on population abundance estimates. In general, this method matched visually scrutinized Atlantic herring regions, but with consistent biases in classifying 38 kHz backscatter. The metrics highlighted spatial and temporal changes in the acoustic landscape, which may be indicative of intra- and inter-annual biological changes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".