Piscivore meets pisces: interactions between grey seals and fish on the Eastern Scotian Shelf and southern Gulf of St. Lawrence
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author. Top-down effects of upper-trophic level predators play important roles in ecosystem structure and functioning. Nevertheless, interactions between pinnipeds and their prey remain poorly understood. This uncertainty has fueled debate on the impact of seal predation on fish stocks. We show that novel combination of acoustic (Vemco Mobile Transceiver, VMT) and GPS technology can be used to determine the spatio-temporal pattern of interactions between grey seals (Halichoerus grypus) and fish species in two marine ecosystems, the Eastern Scotian Shelf and southern Gulf of St. Lawrence, Canada. During four years of study, 16 of 64 adult grey seals recorded 1,117 detections from various fish species including 17 adult Atlantic cod (Gadus morhua), 7 Atlantic salmon (Salmo salar) and one American eel (Anguilla rostrata) implanted with coded acoustics tags. An examination of the spatiotemporal pattern of these interactions suggested that none involved predation. These preliminary results provide proof-of-concept that predators fitted with VMT and GPS tags can provide information on species locations in areas where fixed receiver arrays are not present and allow new insights into the nature of predator-prey interactions in otherwise inaccessible environments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".