First insights into the vertical habitat use of young porbeagles in the north-western Atlantic with implications for bycatch reduction strategies
Bibliographic record
Abstract
Context For threatened marine species, data on their vertical habitat use patterns can reveal risk of interactions with fishing gear and can inform bycatch avoidance strategies. Such data are lacking for young porbeagles (Lamna nasus), which are captured as bycatch in north-western Atlantic fisheries. Aims We aimed to examine temporal patterns in diving and characterise vertical habitat use of young porbeagles during summer and autumn. Methods We used data from short-term (28-day), high-resolution (5-min interval) pop-off satellite tags attached to 14 young (young-of-the-year and 1-year-old) porbeagles to model depth use. Key results Occupied depths ranged from the sea surface to 679 m, with ambient water temperatures of −0.2 to 26°C. Diel period and season were factors related to depth use. Conclusions Sharks exhibited a diel activity pattern characterised by more extensive use of the water column during the day while remaining primarily at the surface at night. Depth use differed between seasons, with summer characterised by greater affinity for surface waters (0–10 m) compared to autumn. Implications Young porbeagles are at risk of interaction with active fisheries on the continental shelf, but interactions may be reduced by setting gear deeper at night or during summer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".