Acoustic characteristics of forage fish species in the Gulf of Alaska and Bering Sea based on Kirchhoff-approximation models
Bibliographic record
Abstract
Acoustic surveys are routinely used to assess fish abundance. To ensure accurate population estimates, the characteristics of echoes from constituent species must be quantified. Kirchhoff-ray mode (KRM) backscatter models were used to quantify acoustic characteristics of Bering Sea and Gulf of Alaska pelagic fish species: capelin (Mallotus villosus), Pacific herring (Clupea pallasii), walleye pollock (Theragra chalcogramma), Atka mackerel (Pleurogrammus monopterygius), and eulachon (Thaleichthys pacificus). Atka mackerel and eulachon do not have swimbladders. Acoustic backscatter was estimated as a function of insonifying frequency, fish length, and body orientation relative to the incident wave front. Backscatter intensity and variance estimates were compared to examine the potential to discriminate among species. Based on relative intensity differences, species could be separated in two major groups: fish with gas-filled swimbladders and fish without swimbladders. The effects of length and tilt angle on echo intensity depended on frequency. Variability in target strength (TS) resulting from morphometric differences was high for species without swimbladders. Based on our model predictions, a series of TS to length equations were developed for each species at the common frequencies used by fisheries acousticians.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".