Atlantic bluefin tuna (<i>Thunnus thynnus</i>) in Greenland — mixed-stock origin, diet, hydrographic conditions, and repeated catches in this new fringe area
Bibliographic record
Abstract
Based on collaboration with the Greenlandic fishing fleet, we document the presence of Atlantic bluefin tuna (Thunnus thynnus) in most years from 2012 to 2018 in the waters east of Greenland (northern Irminger Sea). In total, 84 individuals have been registered as bycatch in the commercial fisheries in Greenland waters, which indicates that the first catch of three individuals in 2012 was not a single extreme observation, but that East Greenland waters have become a new outer limit of an expanded tuna habitat. Genetic analyses indicate that specimens from this region are mostly of Mediterranean origin with a small proportion originating from the Gulf of Mexico stock. Stomach content analysis suggests that the main prey is Atlantic mackerel (Scomber scombrus). The tunas ranged in size from 140 to 270 cm, corresponding to an estimated age range of 5–16 years; most were probably mature. The wide size–age range suggests that many year classes are participating in the migration to this region. Sea temperatures during summer have been above the long-term average in recent years of interest. Summer residence of bluefin tuna in the region could be due to a combination of increasing temperatures and higher overall abundances of both bluefin tuna and a key prey species (Atlantic mackerel).
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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 | 0.001 |
| 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".