Genetic analysis reveals two stocks of skipjack tuna (Katsuwonus pelamis) in the northwestern Indian Ocean
Bibliographic record
Abstract
Skipjack (SJT) ( Katsuwonus pelamis ) is a medium-sized, pelagic, highly dispersive tuna species that occurs widely across tropical and subtropical waters. SJT constitute the largest tuna fishery in the Indian Ocean and are currently managed as a single stock. Patterns of genetic variation in a mtDNA gene and six microsatellite loci were examined to test for stock structure in the northwestern Indian Ocean. 324 individuals were sampled from five major fishing grounds around Sri Lanka and from single sites in the Maldive Islands and the Laccadive Islands. Phylogenetic reconstruction of mtDNA revealed two coexisting divergent clades in the region. Analysis of molecular variance (AMOVA) of mtDNA data revealed significant genetic differentiation among sites (ΦST = 0.2029, P < 0.0001), also supported by spatial AMOVA results. AMOVA of microsatellite data also showed significant differentiation among most sampled sites (FST = 0.0256, P < 0.001), consistent with the mtDNA pattern. STRUCTURE analysis of the microsatellite data revealed two differentiated stocks. While both marker types examined identified two genetic groups, microsatellite analysis indicates that the sampled SJT are likely to represent individuals sourced from discrete breeding grounds that are mixed in feeding grounds in Sri Lankan waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".