Acoustic Classification of Juvenile Pacific Salmon (Oncorhynchus spp) and Pacific Herring (Clupea pallasii) Schools Using Random Forests
Bibliographic record
Abstract
Acoustic surveys are the standard approach for evaluating many fish stocks around the world. The analysis of such survey data requires the accurate echo-classification of target species. This classification is often challenging as many organisms exhibit overlapping characteristics in terms of shape, acoustic amplitude, and behavior. In this study, a random forest approach was used to distinguish juvenile Pacific salmon ( Oncorhynchus spp) from Pacific herring ( Clupea pallasii ) aggregations using the acoustic and morphological characteristics of their echo traces. The acoustic data was collected with an autonomous, multi-frequency echosounder deployed on the seafloor in the Discovery Islands, British Columbia from May to September 2015. The model was able to differentiate juvenile Pacific salmon from Pacific herring with a 98% accuracy. School depth and school mean volume backscattering strength were the most important predictors in determining the school classification. This study supports other publications suggesting that random forests represent a promising approach to acoustic target classification in fisheries science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".