Diet–tissue discrimination factors ( <i>δ</i> <sup>15</sup> N and <i>δ</i> <sup>13</sup> C values) for blood components in Magellanic ( <scp> <i>Spheniscus magellanicus</i> </scp> ) and southern rockhopper ( <scp> <i>Eudyptes chrysocome</i> </scp> ) penguins
Bibliographic record
Abstract
Rationale Analysis of the stable isotope ratios of carbon and nitrogen ( δ 13 C and δ 15 N values) is increasingly being used to gain insight into predator trophic ecology, which requires accurate diet–tissue discrimination factors (DTDFs), or the isotopic difference between prey and predator. Accurate DTDFs must be calculated from predators consuming an isotopically constant diet over time in controlled feeding experiments, but these studies have received little attention to date, especially among seabird species. Methods In this study, aquarium‐housed Magellanic ( Spheniscus magellanicus ) and southern rockhopper ( Eudyptes chrysocome ) penguins were fed a single‐prey source diet (capelin Mallotus villosus ) for eight weeks. Stable isotope ratios ( δ 13 C and δ 15 N values) of penguin blood (cellular component and plasma) and capelin were measured using mass spectrometry and then used to calculate DTDFs for both components of penguin blood by comparison with prey values. Results The DTDFs for plasma were −0.63 ± 0.49 (mean ± SD) and −0.27 ± 0.22 for δ 13 C values, and 2.60 ± 0.50 and 2.78 ± 0.22 for δ 15 N values for Magellanic and southern rockhopper penguins, respectively, while the DTDFs for the cellular component were 1.22 ± 0.03 and 1.26 ± 0.03 for δ 13 C values, and 2.54 ± 0.07 and 2.43 ± 0.17 for δ 15 N values. Conclusions We compare our DTDFs with published values from blood components of penguins and discuss the effects that lipid extraction, sample storage, and diet have on the DTDFs of penguin blood components. This study provides accurate DTDFs of blood components for two seabird species of conservation concern, and is one of the first to provide plasma DTDFs for penguins, which are underrepresented in the seabird literature.
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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 | 0.000 |
| 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".