Stable Carbon and Nitrogen Isotope Discrimination Factors for Quantifying Spectacled Eider Nutrient Allocation to Egg Production
Bibliographic record
Abstract
Nutrient-allocation models based on stable-isotope analysis are used to determine the nutrient sources birds invest in eggs. This approach is particularly useful for birds that migrate between habitats with distinct stable-isotope compositions. A crucial variable is the difference in stable-isotope values of egg tissues relative to diet, so appropriate adjustments can be used in models comparing nutrients from tissues to putative food sources. We established discrimination factors () between the diet and eggs of captive Spectacled Eiders (Somateria fischeri) fed a controlled diet. Relative to diet, values of 13 C were higher for albumen (2.6), yolk protein (2.9), eggshell (13.0), and shell membrane (3.9), and lower for whole yolk (-1.6) and yolk lipid (-3.5). Values of 15 N of egg components were higher relative to diet (albumen 3.7, yolk protein 4.4, shell membrane 4.7, and whole yolk 3.5). Except for egg proteins, these patterns are generally consistent with published values for other birds. We conclude that choice of discrimination factors could markedly affect estimates of source contributions to eggs and so recommend species-specific estimates. We also provide the first reported discrimination factors between the female's diet and embryonic down feathers ( 13 C = 2.1 and 15 N = 5.2). Finally, we determined discrimination factors between lipid and protein in diet sources and eggs, thus enabling consideration of these nutrients separately. Our study enhances the framework for nutrient-allocation modeling in eiders and likely other sea ducks.
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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 | 0.000 | 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) | 0.000 | 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".