Diet–tissue discrimination factors of carbon and nitrogen stable isotopes in blood of Snowy Owl (<i>Bubo scandiacus</i>)
Bibliographic record
Abstract
Analysis of carbon (13C/12C) and nitrogen (15N/14N) stable isotope ratios (hereafter δ13C and δ15N, respectively) in animal tissues is a powerful tool in food-web studies. However, isotopic ratios of prey are not transmitted directly to a consumer, as a diet–tissue discrimination factor (denoted Δ) occurs between sources and consumer’s tissues. An accurate assessment of the diet of a consumer with stable isotopes thus requires that the Δ13C and Δ15N of the studied species are known. Our aim was to establish Δ13C and Δ15N values in the Snowy Owl ( Bubo scandiacus (L., 1758)). Moreover, we assessed the potential effect of ethanol preservation of blood samples on δ13C and δ15N values. We kept four captive adult Snowy Owls on a pure diet of mice for ≥6 weeks. We then collected mouse muscle and blood samples from the owls and analyzed their δ13C and δ15N values. Δ13C and Δ15N values (mean ± SE) for owl blood were +0.3‰ ± 0.2‰ and +1.9‰ ± 0.1‰, respectively. These values are the first discrimination factors ever reported in Strigiformes and are lower, for Δ15N, than those obtained in terrestrial carnivores and other bird species, including falcons. Preservation in ethanol did not significantly affect δ13C and δ15N values.
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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) | 0.001 | 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".