Using multivariate autoregressive state‐space models to examine stock structure of Greenland halibut in the North Atlantic
Bibliographic record
Abstract
Abstract Accurate information on population structure is essential for effective fisheries management. Greenland halibut (Reinhardtius hippoglossoides) in the North Atlantic is managed as four separate offshore stocks. We use Multivariate Autoregressive State‐Space (MARSS) models to assess population structure by means of abundance and biomass trends in four regions (Norwegian Sea, Iceland, Southeast Greenland, and Northwest Atlantic) where three offshore stocks are recognized: (1) Baffin Bay–Davis Strait (Northwest Atlantic stock), (2) Southeast Greenland and Iceland (West Nordic stock (WNS)), and (3) the Barents and Norwegian Seas (Northeast Arctic stock). We formulated model alternatives, using bottom trawl survey data from each region for 1996–2019, to evaluate support for different population structures. Abundance and biomass observations from each region were linked to growth rate parameters in MARSS models and the impact of climate (North Atlantic Oscillation Index) and fishing (commercial catches) on stock dynamics was investigated. Top models identified the Northwest Atlantic as an independent population. Best‐fit models treated Greenland halibut in the WNS as two independent populations (east and west), with potential connections between eastern Iceland and the western Barents Sea. These results suggest a mismatch between current stock perception and management boundaries in the Northeast Atlantic.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".