Multi-year residency and movement patterns of Arctic skate Amblyraja hyperborea, a bycatch species, across an Arctic community fishing ground
Bibliographic record
Abstract
Understanding the residency and movement patterns of at-risk species is critical for effective conservation and management. Due to declining sea ice, Arctic skate Amblyraja hyperborea could be a potentially vulnerable bycatch species in expanding Arctic fisheries targeting Greenland halibut Reinhardtius hippoglossoides . Multi-year acoustic telemetry (2011-2012 and 2014-2016) at depths of 400-1200 m in Cumberland Sound (Nunavut, Canada) revealed that most Arctic skates remained resident or displayed fidelity (29 individuals, 69%) to the location where they were released. Dispersal was generally limited (<20 km; n = 31; 74% of detected individuals), although 11 skates (26%) exhibited higher mobility which could exceed 100 km. Resident skates were present across an annual cycle, experiencing varying bottom water temperatures (0.5-2.6°C), dissolved oxygen levels (2.63-3.99 μmol l -1 ), and sea ice concentrations (0-100%). These findings suggest that Arctic skate distribution in Cumberland Sound is influenced more by sedentary behaviour than by hydrodynamic conditions, with regional residency maintained over multiple years. Concurrent tracking of Greenland halibut revealed contrasting movement patterns: while Arctic skates exhibited residency, Greenland halibut displayed seasonal mobility. Sedentary behaviour increases the vulnerability of Arctic skates to bycatch, as Greenland halibut migrations overlap with resident Arctic skates across Cumberland Sound. Consequently, growth of Greenland halibut fisheries will heighten Arctic skate bycatch risk; thus, local extirpation risks are a concern, requiring fisheries to consider (1) bycatch monitoring strategies, (2) improved handling practices, and (3) an assessment of depth-based mortality risk following capture and release.
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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".