Properties of abundance indices obtained from acoustic data collected by inshore herring gillnet boats
Bibliographic record
Abstract
Acoustic data collection during fishing activities can be used to obtain an abundance index. A simulation, calibrated against an experiment conducted during the Pictou, Nova Scotia, Canada, 1997 inshore herring fishery, is used to understand how survey design affects the properties of abundance indices derived from these data. Two fishing survey protocols and random and systematic transect surveys were simulated. During the complete fishing survey protocol, the simulated survey boat collected acoustic data before and after a management-imposed nightly boat limit was caught. In contrast, during the incomplete fishing survey protocol, data collection was terminated when the boat limit was caught. Properties of abundance indices derived from the fishing and transect surveys were examined over five levels of fish dispersion, two conditions of fish mobility, and in the presence and absence of concurrent fleet fishing. All indices were subject to change caused by changing fish dispersion, but only the incomplete fishing survey index was highly unsatisfactory. The complete fishing survey index is more susceptible to change than the transect indices but displays a lower sampling variation across conditions than the transect indices. We conclude that the complete fishing survey index is a viable alternative to the transect indices.
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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 | 0.004 | 0.027 |
| 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 | 0.001 |
| 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".