Population structure and spatial distribution of porbeagles (Lamna nasus) in Irish waters
Bibliographic record
Abstract
Abstract Porbeagles throughout the North Atlantic have experienced severe population decline through overfishing, with the northeastern population listed as critically endangered. Management of this population is constrained by the paucity of data on porbeagle population structure, distribution and behaviour in this region. Here we use a long-term (47 year) Irish capture-mark-recapture dataset to investigate the population structure, spatial distribution and seasonal movements of this species. From 1970–2017, a total of 268 sharks (9 recaptures) were ID tagged, with most individuals likely being juvenile based on length at maturity estimates (mean total length = 143.9 cm, SD = 35.4). Almost all captures were recorded at three distinct locations near angling hubs along the south, west and north coasts with catches peaking in August. Long-term trends in capture date indicated a shift towards earlier capture dates in the northern site (n = 153). Our findings suggest Irish waters may act as a persistent summer aggregation site for juveniles, which show evidence for seasonal site fidelity, returning to nearby locations between years. These findings demonstrate the utility of such programmes, which can be implemented, with minimal expense by engaging with the angling sector, to elucidate the population structure and distribution of wide-ranging fish species.
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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) | 0.002 | 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".