495 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
Abstract The objective of this study was to evaluate acid detergent lignin (ADL), and amylase-treated ash-corrected undigestible neutral detergent fiber after 240 h of ruminal in vitro incubation (uNDF), and near infrared spectroscopy (NIRS) scanning of feces as methods to estimate the digestibility of diets varying in forage quantity and quality when fed to beef cattle. Five total collection (TC) digestibility studies examined 17 different diets and provided individual fecal samples and the corresponding apparent total tract digestibility of nutrients (n = 229). Feed, orts, and fecal samples were analyzed for dry matter (DM), organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), ADL, and uNDF. Previously developed fecal NIRS digestibility calibrations were expanded with dried and ground samples using a FOSS D3F scanning monochromator (FOSS, Eden Prairie, MN). Marker estimated and NIRS predicted nutrient digestibility coefficients were regressed against those determined by TC and goodness-of-fit statistics were applied. Mean concentrations of ADL and uNDF in diets ranged from 23.4 to 96.4 g/kg DM and 67.7 to 200 g/kg DM, respectively, with mean fecal recoveries of 94.2% (SD ± 15.7%) for ADL and 87.5% (SD ± 11.1%) for uNDF. Regression fit statistics between NIRS and TC were not different from one (P > 0.05), with R2 > 0.83 except for ADF digestibility (R2 = 0.63). In comparison, regression statistics between internal markers and TC were poorer with R2 ranging from 0.21 to 0.78. Concordance correlation coefficients (CCC) between NIRS and TC were greater than 0.90 for DM, OM, N, and aNDFom digestibility, and 0.77 for ADF digestibility. The CCC between ADL and TC ranged between 0.63 and 0.82, and between uNDF and TC from 0.26 to 0.78. Correction bias (Cb) was high for all parameters (Cb > 0.76) except for ADF digestibility (Cb = 0.58) as estimated by uNDF. The mean square error of prediction (MSEP) for NIRS predicted digestibility were lower (< 10.7%) than those estimated by internal markers, and most of the error was attributed to random bias (> 97.9%). Random bias for ADL was also greater (>76.1%); however, for uNDF the error was more evenly partitioned into mean bias (46.4% to 49.4%) and random bias (30.4 to 41.9%). Digestibility predictions from NIRS scanning of feces were more accurate and precise than when estimated using the internal markers ADL and uNDF. In the absence of NIRS, ADL estimations appear to be more precise and accurate than uNDF for forage-based diets, with uNDF possibly of greater value for estimating the digestibility of high concentrate diets.
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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 | 0.001 | 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".