Stable isotopes of C and N differ in their ability to reconstruct diets of cattle fed C3–C4 forage diets
Bibliographic record
Abstract
Abstract Stable isotopes are useful for estimating livestock diet selection. The objective was to compare δ13C and δ15N to estimate diet proportion of C3–C4 forages when steers (Bos spp.) were fed quantities of rhizoma peanut (Arachisglabrata; RP; C3) and bahiagrass (Paspalumnotatum; C4).Treatments were proportions of RP with bahiagrass hay: 100% bahiagrass (0%RP); 25% RP + 75% bahiagrass (25%RP); 50% RP + 50% bahiagrass (50%RP); 75% RP + 25% bahiagrass (75%RP); and 100% RP (100% RP). Feces, plasma, red blood cell (RBC), and hair were collected at 8-days intervals, for 32 days. Two-pool mixing model was utilized to back-calculate the proportion of RP based on the sample and forage δ13C or δ15N. Feces showed changes using δ13C by 8 days, and adj. R2 between predicted and observed RP proportion was 0.81 by 8 days. Plasma, hair, and RBC required beyond 32-days to reach equilibrium, therefore were not useful predictors of diet composition during the study. Diets were best represented using fecal δ13C at both 8-days and 32-days. By 32-days, fecal δ15N showed promise (R2 = 0.71) for predicting diet composition in C3–C4 diets. Further studies are warranted to further corroborate fecal δ15N as a predictor of diet composition in cattle.
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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 | 0.001 |
| 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".