Feasibility of using growth band counts in age determination of four crustacean species in the Northern Atlantic
Bibliographic record
Abstract
The age information of commercially important species is crucial in fisheries management. Age of various fish and molluscan species has routinely been determined by counting annual growth bands deposited within the hard structures. In crustaceans such structures were previously believed to be lost and replaced due to molting. However, a technique was recently developed to use growth bands deposited in hard structure retained through molting as an age indicator. In the present study, the applicability of the novel technique is investigated for four crustacean species collected from Northern Atlantic for the first time: European lobster, Homarus gammarus (Linnaeus, 1758); Norway lobster, Nephrops norvegicus (Linnaeus, 1758); Atlantic rock crab, Cancer irroratusSay, 1817; and northern shrimp, Pandalus borealis (Krøyer, 1838). The gastric mill ossicles in the first three species were processed to show the growth bands while the eyestalk was used in the shrimp species. Four growth bands were visible in European lobster hatched in a Norwegian hatchery and maintained alive for four years before prior processing. Band counts in the other three species were identical to size-at-age interpretation determined from length-frequency analysis. Validation of the periodicity of annual deposition of growth bands is essential before applying the technique on a wider scale.
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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 | 0.001 | 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 | 0.000 | 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".