Growth analysis and age validation of a deepwater Arctic fish, the Greenland halibut (Reinhardtius hippoglossoides)
Bibliographic record
Abstract
The accuracy of age interpretations on a deep-sea, Arctic fish species, the Greenland halibut ( Reinhardtius hippoglossoides ) was tested using several age validation methods. Consistent annual growth increments were either not formed or not visible in either whole or sectioned otoliths from three fish marked with oxytetracyline and recaptured after 2–4 years at liberty. Bomb radiocarbon assays based on a local reference chronology indicated that both whole and sectioned otoliths underestimated age by 1–15 years, with an average of 6 years. Growth rates estimated using the tag recapture model GROTAG were consistent with growth rates based on the radiocarbon assays and were less than half that of previously reported growth rates. The failure of otolith sections to provide an accurate age is unusual, but may be symptomatic of very slow-growing species with unusually shaped otoliths. Greenland halibut living in the deep-sea, Arctic environment are slower growing and longer lived than previously suspected, suggesting that the age-structured basis for current fisheries management warrants careful examination. Our results highlight the importance of using rigorous tests of ageing accuracy for exploited species and confirm that such age validation methods can be applied successfully in challenging environments such as the deep sea or the Arctic.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".