Seasonal Trends in Groundfish Discards on Georges Bank: Implications for Adaptive Quota Management
Bibliographic record
Abstract
Abstract For fisheries to be sustainable, management should account for all major sources of fishing mortality. On the Canadian side of Georges Bank, landings and discards of transboundary stocks of Atlantic Cod Gadus morhua, Haddock Melanogrammus aeglefinus, and Yellowtail Flounder Limanda ferruginea are monitored against quotas that are shared by the Canadian groundfish and scallop (sea scallop Placopecten magellanicus) fisheries to limit fishing mortality. The shared quota allocations are managed using an adaptive quota management system; quota can be redistributed between the fisheries during the season to maximize fishing opportunities, while still respecting the overall catch limits. However, the redistribution relies on estimates of the total end-of-year discards of Atlantic Cod, Haddock, and Yellowtail Flounder from the scallop fishery. Here, we evaluated and compared two approaches for projecting end-of-year discards within season: an empirical method based on scallop fishery landings and a seasonal modeling approach. Seasonal trends in discards were identified, with discards of all three groundfish species highest in April or May. The seasonal models out-performed the empirical landings-based projection method, and our results demonstrate that accounting for seasonal patterns in discards better informs risk-based fishery management decisions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".