From diet to hair and blood: empirical estimation of discrimination factors for C and N stable isotopes in five terrestrial mammals
Bibliographic record
Abstract
Abstract Carbon and nitrogen stable isotope ratios are used widely to describe wildlife animal diet composition and trophic interactions. To reconstruct consumer diet, the isotopic differences between consumers and their diet items—called the trophic discrimination factor (TDF)—must be known. Proxies of diet composition are sensitive to the accuracy of TDFs. However, specific TDFs are still missing for many species and tissues because only a few controlled studies have been carried out on captive animals. The aim of this study was to estimate TDFs for hair and blood for carbon and nitrogen stable isotopes for caribou, moose, white-tailed deer, eastern coyote, and black bear. We obtained stable isotope ratios for diet items, hair, and blood samples, of 21 captive adult mammals. Diet–tissue discrimination factors for carbon in hair (∆ 13CLE) ranged from 0.96‰ to 3.72‰ for cervids, 3.01‰ to 3.76‰ for coyote, and 5.15‰ to 6.35‰ for black bear, while nitrogen discrimination factors (∆ 15N) ranged from 2.58‰ to 5.95‰ for cervids, 2.90‰ to 3.13‰ for coyote, and 4.48‰ to 5.44‰ for black bear. The ∆ 13CLE values in coyote blood components ranged from 2.20‰ to 2.69‰ while ∆ 15N ranged from 3.30‰ to 4.41‰. In caribou serum, ∆ 13CLE reached 3.34 ± 1.28‰ while ∆ 15N reached 5.02 ± 0.07‰. The TDFs calculated in this study will allow the evaluation of diet composition and trophic relationships between these five mammal species and will have important implications for the study of endangered caribou populations for which the use of noninvasive tissue sampling is highly relevant.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".