Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle
Bibliographic record
Abstract
The objective of this study was to evaluate the use of fecal near-infrared reflectance spectroscopy (NIRS) profiling to predict diet nutritional characteristics and voluntary DMI in beef cattle. Fecal samples were collected for growing cattle across 11 experiments in which individual animal performance and DMI was measured. Dried and ground fecal composite samples collected from each animal were subjected to fecal NIRS analysis by a Foss NIRS 6500 scanning monochromator (Foss, Eden Prairie, MN) at the Grazingland Animal Nutrition Laboratory (Temple, TX). Fecal spectra were then used to develop equations to predict diet composition (trials 1 to 11; n = 408), digestibility (trials 1 to 5; n = 155), and DMI (trials 1 to 11; n = 408). Coefficients of determination for calibration (R2c) and cross-validation (R2cv) for prediction of diet nutritional characteristics were lower for NDF (R2c = 0.85; R2cv = 0.82) than for CP (R2c = 0.90; R2cv = 0.88). For the prediction of DMI, R2c and R2cv ranged from 0.69 and 0.67 for the prediction of trial-average DMI to 0.76 and 0.73 for the prediction of fecal-collection-period DMI. While the R2c and R2cv obtained for the prediction of DMI were lower than those obtained for the prediction of diet composition or digestibility, fecal NIRS prediction equations for DMI were successful in predicting the mean DMI of groups, as no differences were found for the prediction of fecal-collection-period DMI (Diff. = 1.10; P = 0.72) or trial DMI (Diff. = -0.47; P = 0.86).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".