Identification of echoes from demersal aggregations of hake, <i>Merluccius productus</i>, in the Gulf of California
Bibliographic record
Abstract
Abstract The distribution of Pacific hake, Merluccius productus, extends from Northern Canada to Southern Baja California and into the Gulf of California (GC), Mexico. In an emergent fishery in the Northern GC, hake dominates finfish catches from February to May. Developing management strategies aiming to make this fishery sustainable requires estimates of abundance, which can be provided by the acoustic-trawl method. To identify echoes from daytime demersal aggregations of hake, we use ranges of the sum and difference of mean volume backscattering strength (SV) measured at 38 and 120 kHz. More than 90% of the summed 38-kHz volume backscattering coefficient (sV38) associated with hake-only catches are retained using −4.2 < SV120 – SV38 < 0.9 and −122.0 < SV38 + SV120 < −110.4 dB. These ranges mostly differ for cusk-eel, family Ophidiidae, and scorpionfishes, family Scorpaenidae, which are frequently caught with hake in the Northern GC, but overlap completely for sharks, family Triakidae. To estimate the accuracy of the putative hake backscatter, we present a novel method that accounts for the spatial distribution of area backscattering coefficient, the sensitivity and specificity of the hake-identification ranges, and the acoustic-proportions of hake and the other species.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".