Isotopic Discrimination (δ<sup>15</sup>N, δ<sup>13</sup>C) in Captive and Wild Common Murres (<i>Uria aalge</i>) and Atlantic Puffins (<i>Fratercula arctica</i>)
Bibliographic record
Abstract
Studying the diet of consumers using stable isotopes provides insight into the foraging ecology of individuals and species. To accurately reconstruct the integrated diet of animals using stable isotope values, we must quantify diet-tissue discrimination factors (DTDFs), or the way in which stable isotopes in prey are incorporated into the tissues of consumers. To quantify DTDFs, controlled experiments are needed, whereby consumers are fed a constant diet. However, relatively few controlled-diet studies have been conducted for seabirds. In this study, captive adult Atlantic puffins (Fratercula arctica) and common murres (Uria aalge) were fed a two-source diet of capelin (Mallotus villosus) and Atlantic silverside (Menidia menidia) to determine the DTDFs for the cellular component of blood and plasma for both δ15N and δ13C. The DTDFs for the cellular component (Δ15N: 2.80±0.28; Δ13C: 1.21±0.22) and plasma (Δ15N: 1.72±1.03; Δ13C: −0.18±0.56) of puffins were similar to those for the cellular component (Δ15N: 2.91±0.18; Δ13C: 1.09±0.23) and plasma (Δ15N: 2.18±0.77; Δ13C: −0.70±0.18) of murres. We reconstructed the diet of wild murres and puffins breeding on the northeastern coast of Newfoundland using previously published DTDFs and estimated DTDFs from our feeding experiment. Reconstructed dietary proportions supported a priori knowledge of diet, although outputs were sensitive to the DTDF used. Despite the similarity of our DTDFs for puffins and murres, along with the similarity of our DTDFs with those of other seabird species, our sensitivity analysis revealed considerable differences among resultant dietary contributions from mixing models, further highlighting the importance of using species- and tissue-specific DTDFs to enhance knowledge in the foraging ecology of seabirds using stable isotopes.
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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 | 0.000 | 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) | 0.001 | 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".