Can a bottom-moored echo sounder array provide a survey-comparable index of abundance?
Bibliographic record
Abstract
A small number of stationary echo sounders have the potential to produce abundance indices where fish repeatedly occupy localized areas (e.g., spawning grounds). To investigate this possibility, we deployed three trawl-resistant moorings with a newly designed autonomous echo sounder for ∼85 days during the walleye pollock (Gadus chalcogrammus) spawning season in Shelikof Strait, Alaska. Backscatter observed from the moorings was highly correlated with ship-based acoustic surveys, suggesting that the mooring observations reflect abundance over much larger areas than the observation volume of the acoustic beam. A retrospective analysis of a 19-year time series of prespawning walleye pollock surveys was used to select mooring locations and determine that three to five moorings can produce an index of walleye pollock backscatter comparable to that produced by a ship-based survey covering ∼18 000 km2 (mean prediction error of <11% for five moorings). The three moorings deployed in Shelikof Strait yielded a backscatter estimate that was within ∼15%–20% of that observed during the survey. Thus, it appears feasible to design a relatively sparse mooring array to provide abundance information and other aspects of fish behavior in this environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".