Geographic variation of golden redfish (Sebastes marinus) and deep-sea redfish (S. mentella) in the North Atlantic based on otolith shape analysis
Bibliographic record
Abstract
Abstract The unresolved interrelationships of North Atlantic redfish stocks have prevented adaptive fisheries assessment and management in the past. Otolith shapes of golden redfish (S. marinus) and deep-sea redfish (S. mentella) were analysed for geographic variation within the entire distribution range in the North Atlantic, in order to evaluate this technique for stock separation. Multivariate analysis of elliptical Fourier shape descriptors revealed high similarity of S. marinus within the central North Atlantic areas (West and East Greenland, Iceland) that were relatively well separated from the Flemish Cap and Barents Sea samples. A similar separation of the Barents Sea area was also observed for S. mentella, whereas the western (Flemish Cap, Davis Strait) and central areas were overlapping to a greater extent. The overall classification rate given by discriminant analysis was poor for both species (<50%) but increased to 72–74% by combining sampling areas to regions (west, central, east). Geographic variation in otolith shapes of both redfish species suggests a separation of the Northeast Arctic stocks (Barents Sea) of both species from the other redfish stocks assessed within ICES and NAFO, whereas similarities observed for the highly migratory S. mentella give reason for integrated management of demersal and pelagic occurrences of this important fisheries resource that straddles the ICES/NAFO boundaries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".