Estimating the biomass of a mixed species complex using hydroacoustics and catch data from the Bay of Fundy and Scotian Shelf summer ecosystem survey
Bibliographic record
Abstract
An acoustic-based index of biomass was developed due to concerns that the existing bottom-trawl index for western component pollock (Pollachius virens) was inaccurate and imprecise. Acoustic data were recorded during annual summer ecosystem surveys. Pollock and other common groundfish could not be readily identified from echo-traces within echograms. Survey catch data were analyzed concurrently with acoustic data to estimate biomass from echo-integrals contributed by the six most commonly caught fish species: pollock, Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus), redfish (Sebastes fasciatus), Atlantic herring (Clupea harengus), and spiny dogfish (Squalus acanthias). A cluster analyses that used a Bayesian conditional probability classified homogeneous regions based on species composition. Species composition, nautical area scatter coefficient, and backscattering cross-section of fishes for regions were used to calculate the biomass of fishes. Acoustic indices were considered as appropriate as bottom-trawl indices for fisheries management advice because the bias and variance were similar between the indices when analyzed using repeated K-fold cross-validation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".