Fishery Acoustic Indices For Assessing Atlantic Herring Populations
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.A method for measuring the spatial and temporal distribution of fish school densities and exploitation rates using fishery collected acoustic data and voronoi polygons to estimate biomass is described. A herring purse seiner fishing on non-spawning feeding aggregations, and a herring gillnetter fishing on smaller, highly dense spawning aggregations, in the southern Gulf of St. Lawrence, Canada, collected acoustic data during regular fishing activity for this study. An individual boat with data collected in this manner was found to represent trends in the entire fleet. There was a threshold density beyond which exploitation rates remained low. This threshold provides managers with a method for identifying and eliminating spatial and temporal trends in high exploitation rates and preventing overfishing. A simulation model calibrated with data from the Pictou 1997 inshore gillnet fishery compared the properties of abundance indices derived from fishery acoustic data to those derived from survey indices. The indices were examined over five fish distribution types ranging from a single spike to a uniform flat distribution, four conditions of fishing and fish movement, and sixteen stock sizes for each of these distribution and conditions. These data are suitable for deriving abundance indices provided the searching covers the entire temporal and spatial distribution of the population. The fishery acoustic abundance indices provide a basis for adopting a decision rule management strategy, as an alternative to the current F0.1 management strategy for herring populations. Decision rules would allow individual spawning components to become the basic management unit and would result more responsive management system for this fishery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".