Comparison of two internal markers and near-infrared spectroscopy (NIRS) for predicting nutrient digestibility in beef cattle offered diets varying in forage quantity and quality
Bibliographic record
Abstract
The objective of this study was to evaluate acid detergent lignin (ADL), amylase-treated ash-corrected undigestible neutral detergent fiber (uNDFom), and near-infrared spectroscopy (NIRS) of feces as methods to estimate the digestibility of beef cattle diets varying in forage quantity and quality. Five digestibility studies provided individual fecal samples and corresponding apparent total tract digestibility (TTD) of nutrients ( n = 229). An NIRS calibration was expanded with dried and ground samples and calibration performance evaluated using internal cross-validation. Mean fecal recovery of markers were 94.2% (SD ± 15.7%) for ADL and 87.5% (SD ± 11.1%) for uNDFom. The R2 between NIRS and TTD was greater than 0.83 except for ADF digestibility ( R2 = 0.63); however, R2 < 0.78 for internal markers. Concordance correlation coefficients between NIRS and TTD were greater than 0.77, between 0.63 and 0.82 for ADL, and 0.26 to 0.78 for uNDFom. Correction bias (Cb) was greater than 0.76 except for ADF digestibility (Cb = 0.58) as estimated by uNDFom. The mean square error of prediction for NIRS predicted digestibility were lower (<10.7) than those estimated by internal markers. Digestibility predictions from NIRS were more accurate and precise than estimated using the internal makers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".