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 (δ13C) and nitrogen (δ15N) 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 δ13C, and relative trophic positions (TP, based on δ15N) ranged from 3.0 ( Mallotus villosus ) to 3.8 (e.g., Brosme brosme ). Greenland shark δ13C indicated feeding on benthic and pelagic resources, with a high input of pelagic carbon, and δ15N indicated a relative TP of 4.3. Log[Hg] increased with δ15N (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 δ15N and δ13C-assigned resource use than by δ15N alone. Hg was useful for supporting the TPs suggested by δ15N, 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 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".