Impact of survey design changes on stock assessment advice: sea scallops
Bibliographic record
Abstract
Abstract Smith, S. J., and Hubley, B. 2014. Impact of survey design changes on stock assessment advice: sea scallops. – ICES Journal of Marine Science, 71: 320–327. Annual surveys of marine resources are used to monitor changes in population composition and abundance. Improvements in the performance and coverage of these surveys can readily be evaluated for the surveys themselves but should also be considered in the context of the stock assessment models that use the estimates from these surveys. For those surveys based on a probability design, improvements in the probability design are usually evaluated with respect to the resultant increase in precision of the survey estimates. Survey precision estimates can be included in many stock assessment models as observation error, as long as the process error component of the model is also identified. Advice on catch levels for sea scallop populations (Placopecten magellanicus) around Nova Scotia is developed using a Bayesian state space assessment model in which both observation and process error terms have been defined. Information on survey estimates of precision are included in the observation error component of the assessment model and the impacts of changes in survey precision on the provision of advice can be evaluated in terms of reference points and management advice. The sensitivity of stock assessment advice to changes in the level of precision of survey estimates was evaluated for three scallop fisheries around Nova Scotia. The results indicated that the impact of the changes depended upon the degree of concurrence between the annual changes in biomass as observed from the survey and those predicted by the model.
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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 | 0.032 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".