Identifying nonproportionality of fishery-independent survey data to estimate population trends and assess recovery potential for cusk (<i>Brosme brosme</i>)
Bibliographic record
Abstract
Cusk ( Brosme brosme ) was designated as “threatened” by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) in 2003, based on an estimated 93% decline between 1970 and 2001 from the Fisheries and Oceans Canada (DFO) Scotian Shelf summer bottom trawl survey index. We combined this index with a fishery-dependent longline index in a Bayesian surplus production state-space model to estimate population trends and the recovery potential of western Scotian Shelf cusk. We tested for index nonproportionality using a power curve function in the observation model and identified potential hyperdepletion for cusk in the trawl survey index. We estimate a 59% decline in cusk biomass between 1970 and 2001, and a 64% decline from 1970 to 2007. Although population projections indicate the current landing limit should lead to population recovery, robustness tests suggest the biomass projections and recovery time lines are overly optimistic. Simulations showed that incorporating multiple indices increases power to recapture model parameters and failure to account for index nonproportionality results in biased parameter estimates. We suggest that nonproportionality of fishery-independent indices must be considered when determining the population status of data-poor 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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".