Stable carbon and nitrogen isotope discrimination factors from diet to blood plasma, cellular blood, feathers, and adipose tissue fatty acids in Spectacled Eiders (Somateria fischeri)
Bibliographic record
Abstract
Stable isotope analyses of animal tissues can be used to infer diet through application of mixing models. An important component in a mixing model is the incorporation of stable isotope discrimination factors so that isotopic shifts between diet and tissues built from the diet can be accounted for when comparing tissues to potential food sources. We determined the stable carbon and nitrogen isotopic discrimination factors between lipid-free diet and blood plasma, cellular blood, and adult chest contour feathers for captive female Spectacled Eiders ( Somateria fischeri (Brandt, 1847)). Mean discrimination factors for blood components and feathers were either similar or slightly larger compared with previously studied species. Additionally, we determined the stable carbon isotope discrimination factors between dietary lipids and adipose tissue fatty acids using three adipose tissue biopsies from captive male Spectacled Eiders that were fed three different diet treatments. Isotopic signatures of adipose tissue fatty acids closely reflected shifts in the diet and were either similar to or increased relative to diet. Our study provides a foundation for research using tissues as end-members in stable isotope nutrient allocation models and foraging ecology studies of Spectacled Eiders, and will provide the most applicable isotope data to date for sea ducks.
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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 | 0.001 |
| 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".