Management strategies for spasmodic stocks: a Canadian Atlantic redfish fishery case study
Bibliographic record
Abstract
There exist few recommendations for managing stocks with spasmodic recruitment, despite such stocks being not uncommon. Management procedures (MPs), developed for two species of redfish (Sebastes mentella and Sebastes fasciatus) in eastern Canada, are recommended for setting catch limits during periods of high and low abundance. A well-designed fishery-independent trawl survey is essential to provide advance warning of strong recruitment events and project future recruitment. Under an “inventory management” strategy, a more appropriate aim in spasmodic stocks may be to maximize the number of years with “good catches,” instead of maximizing total catches, as is traditionally considered in management strategy evaluation (MSE). Following a spasmodic recruitment event, an empirical harvest control rule based on larger fish delays the harvest of large cohorts by a few years, targets more commercially valuable fish sizes, and reduces the risk of growth overfishing. Capped MPs produced longer periods of large catches than uncapped MPs. MPs allowed for low harvests during periods of low abundance, thus avoiding unnecessary hardship in the industry. MPs evaluated here could be good candidates for other stocks with similar or less extreme recruitment variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".