Unsupervised classification increases precision in acoustic broadband target strength measurements of mesopelagic fish
Bibliographic record
Abstract
Mesopelagic fish are widespread and abundant in global oceans, contributing to nutrient cycling and potential future fisheries. Estimating their distribution and abundance is challenging due to limitations of ship-based echosounders and trawling. Submersible broadband acoustic probes are often used for precise target measurements due to increased range resolution and the possibility to use higher frequencies with expanded spectra; however, broadband measurements can introduce undesired signals into the data. Using an unsupervised outlier detection approach, we enhanced the selectivity of broadband acoustic targets of mesopelagic fish in the Labrador Sea. We observed high variability in frequency response within insonified volumes and echo-traces. Applying an outlier detection algorithm, we filtered anomalous signals, improving density estimates by reducing positive skew up to 35%. Our approach increases measurement precision and provides insight into broadband echosounder applications for mesopelagic fish assessments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".