Active predation by Greenland shark Somniosus microcephalus
Bibliographic record
Abstract
Dansk Havforskermøde 2013 Julius Nielsen, Rasmus Hedeholm, Malene Simon og John Fleng Steffensen The Greenland shark is ubiquitous in the northern part of the North Atlantic ranging from eastern Canada to northwest Russia . Although knowledge is scarce it is believed to be abundant and potentially important part of the ecosystem. Whether Greenland sharks in general should be considered opportunistic scavengers or active predators is therefore important in understanding ecosystem dynamics. Due to its sluggish appearance and a maximum reported swimming speed of 74 cm per second scavenging seems the most likely feeding strategy. However, recent studies suggest that Greenland sharks in some areas feed actively upon seals . Feeding ecology is poorly described in Greenland waters. In this study we provide information on feeding habits of 29 sharks caught in Greenland waters in the summer 2012 and show that the sharks catch epi-benthic species with Atlantic cod being the most important (% IRI = 56 ), followed by squid (% IRI= 13 ) and wolf fish (IRI=4). Furthermore seal was found in 50 % of all stomachs (% IRI= 13). In addition to providing new knowledge of feeding habits of this species in Greenland waters, we suggest the results show that the Greenland catches the majority of its prey by active predation.
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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 | 0.000 | 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".