Tag attachment innovation on spurdog (<i>Squalus acanthias</i>) reveals year‐round coastal association of pregnant females in northeastern Atlantic waters
Bibliographic record
Abstract
The spurdog (Squalus acanthias Linnaeus, 1758) is a globally distributed squaliform shark that has historically been overfished but is now recovering in the northeast Atlantic. Data series on spurdog movement and habitat use have been somewhat limited to research surveys due to challenges associated with electronic tagging. Here, we offer a revised attachment method for externally attached pop-up satellite archival tags that was successful in long-term deployments on pregnant females. Twenty-one spurdogs were tagged in two fjord systems in western Norway for an average of 243 days and provided new details about their behaviour, demonstrating affinity for coastal habitat based on the pop-up locations and recovery positions of the tags (84% within 40 km from tagging location), and depth-temperature profiles. It is likely that parturition therefore occurs in these coastal waters, making them important to the life cycle of this species. The realized depth niche of tagged individuals suggested that surveys may miss sharks if they do not cover the full water column because the sharks used large parts of the water column and spent much time in shallower waters, albeit with seasonal variations (deeper and shallower in winter and summer, respectively). Adoption of this tagging method and combination with movement data from acoustic transmitters will help to better resolve the behaviour of this species as it transitions from a species at risk to a managed fishery. Such studies will provide a more comprehensive understanding of the species' habitat requirements that will empower better informed protections against a return to the red list of threatened 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".