Discriminant Classification of Fish and Zooplankton Backscattering at 38 and 120 kHz
Bibliographic record
Abstract
Abstract Acoustic scattering layers were evaluated for species classification by means of 38‐ and 120‐kHz mean volume backscattering strength ( ) collected during a 1995 acoustic–trawl survey of Pacific hake Merluccius productus off the west coasts of the United States and Canada. Scattering layers selected for analyses were shallower than 150 m and were analyzed with a −79‐decibel (dB) integration threshold. Pacific hakes, euphausiids, and Pacific hake–euphausiid mixes dominated the layers. Other scatterers (unidentified, noneuphausiid, or non—Pacific hake sources) were included in the analyses. The overall mean volume backscatter difference (Δ = 120 kHz – 38 kHz) was computed for each species category, and results varied depending on the species composition of the scattering layer (i.e., Pacific hakes = −7.1 dB, euphausiids = 11.9 dB, Pacific hakes–euphausiids = 3.5 dB, and other species = 0.1 dB). Discriminant function analysis of 120 kHz and 38 kHz separated echoes originating from each of the dominant scattering layers. Backscatter was then classified into species groups with a quadratic discriminant classification model, which obtained an overall correct classification rate of 84%. The use of multiple frequencies and these analytical methods (e.g., frequency differencing and discriminant classification functions) can provide an efficient and objective means of classifying sound‐scattering layers composed of different taxonomic groups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".