Diet and resource use among Greenland sharks (Somniosus microcephalus) and teleosts sampled in Icelandic waters, using δ<sup>13</sup>C, δ<sup>15</sup>N, and mercury
Bibliographic record
Abstract
Stable carbon (δ 13 C) and nitrogen (δ 15 N) isotopes and total mercury (Hg) were used to investigate diet and resource use among Greenland sharks ( Somniosus microcephalus ) and 14 teleosts inhabiting Icelandic waters. Greenland shark stomachs contained 11 of the teleosts sampled, along with other fishes and marine mammal tissues. Teleost resource use ranged from pelagic (e.g., Argentina silus ) to benthic (e.g., Anarhichas lupus ) based on δ 13 C, and relative trophic positions (TP, based on δ 15 N) ranged from 3.0 ( Mallotus villosus ) to 3.8 (e.g., Brosme brosme ). Greenland shark δ 13 C indicated feeding on benthic and pelagic resources, with a high input of pelagic carbon, and δ 15 N indicated a relative TP of 4.3. Log[Hg] increased with δ 15 N (i.e., TP) from teleosts to Greenland sharks and was higher in offshore vs. inshore teleosts. Linear regressions revealed that log[Hg] was better described by both δ 15 N and δ 13 C-assigned resource use than by δ 15 N alone. Hg was useful for supporting the TPs suggested by δ 15 N, and the higher Hg in offshore fishes could help explain the high Hg of Greenland sharks. Results from this study demonstrated the potential use of Hg as a dietary tracer in marine fishes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".