DIET‐TISSUE FRACTIONATION OF STABLE CARBON AND NITROGEN ISOTOPES IN PHOCID SEALS
Bibliographic record
Abstract
Abstract Diet‐tissue isotopic fractionation of carbon (C) and nitrogen (N) isotopes in short‐ and longer‐term diet integrators of diet (i. e., blood serum and red cells), that involve non‐invasive sampling techniques was examined using three species of phocid seals (harbor seals, gray seals, and harp seals) fed a known diet. Variability in diet‐tissue fractionation values within and between species was also scrutinized to determine the legitimacy of using values obtained from one species to explore trophic positions and diets of other related species. All captive seals raised on a constant diet had tissues enriched in 13C and 15N relative to their diet. Diet‐tissue isotopic fractionation values were generally consistent among conspecifics and among phocid species for a given tissue. Trophic isotopic enrichment in 13C was significantly higher in red blood cells (+1.5%±) than in blood serum (+0.8%±), whereas the reverse was observed for nitrogen isotopes (+1.7%± in red cells vs. +3.1%± in serum). However, 13C‐depleted lipids were not extracted from blood tissues in this study. This results in a downward bias in the diet‐tissue fractionation factors for carbon for both red cells and blood serum, particularly the latter because of their significantly higher lipid contents (x̄± SD = 14.6 ± 2.3%; n= 20; red blood cells 3.8 ± 0.9%±; n= 50, muscle 7.7 ± 2.0; n= 21) in marine mammals.
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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) | 0.000 | 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 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".