Using multivariate state-space models to examine commercial stocks of redfish (<i>Sebastes</i> spp.) on the Flemish Cap
Bibliographic record
Abstract
There are three different species of redfish (Sebastes spp.) in the waters of the Flemish Cap (Division 3M, NAFO Regulatory Area): S. fasciatus, S. mentella, and S. norvegicus. Historically, S. fasciatus and S. mentella have been managed together as a single stock because of similar biology and difficulty in species identification. Here we use multivariate autoregressive state-space models to examine the abundance trajectories of the three species and to determine whether they can be treated as a single stock for management purposes or whether they should be treaty separately. We also included covariates to evaluate relationships with climate, commercial catch, and the abundance of predators and (or) competitors and prey. We did two separate analyses: (i) a single-period analysis over the full time series and (ii) a blocked, two-period analysis over different regulatory periods. In both analyses, the best-fit model included separate trajectories for each species at each depth but one overall stock growth rate; both also included commercial catches as a covariate. These analyses suggest that a single assessment for the Sebastes complex is acceptable.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".