Development and application of an empirical multifrequency method for backscatter classification
Bibliographic record
Abstract
We evaluated the feasibility of identifying major acoustic scatters in North Pacific ecosystems based on empirical measurements of relative frequency response. Acoustic measurements in areas where trawl catches were dominated by single taxa indicated that it might be possible to discern among key groups of scatterers such as fish with gas-filled swimbladders, euphausiids, myctophids, and jellyfish. To establish if walleye pollock ( Theragra chalcogramma ), a key species in the ecosystem, can be separated reliably from other groups under prevailing conditions, we developed a method based on the normal deviate (or Z score) to identify backscatter consistent with the pollock relative frequency response. We evaluated the performance of the method by comparing it with the traditional method of species identification (i.e., directed trawl catches and subjective interpretation of echograms) during five large-scale acoustic surveys of the eastern Bering Sea. Pollock abundance estimates employing the multifrequency method were highly correlated with those using the traditional method, which indicates that the multifrequency method performs well in this situation. In this environment, multifrequency methods will allow more inferences to be drawn when direct sampling of organisms is limited and will also complement existing abundance surveys by improving species classification and providing information about key nontarget 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".