Characterization of the variation in the daily excretion of faecal constituents and digestibility predictions in beef cattle fed feedlot diets using near-infrared spectroscopy
Bibliographic record
Abstract
Six heifers were individually housed and assigned to once (FF1) or twice (FF2) daily feeding regimes over backgrounding and finishing periods. Following adaptation, total faecal collections were conducted at 4-h intervals and at 24-h intervals over 4 d, and near-infrared spectroscopy (NIRS) was used to predict faecal organic matter (OM), starch, nitrogen (N), neutral detergent fibre (NDF), acid detergent fibre (ADF), and acid detergent lignin (ADL). At each interval, NIRS calibrations were used to estimate faecal constituents and ADL to calculate apparent (aTTD) and estimated (eTTD) total tract digestibility. Faecal dry matter (DM) (%), NDF, and ADF varied among 4-h interval samples in the backgrounding period and faecal DM, starch, NDF, ADF, and ADL in the finishing period. Faecal starch was able to predict aTTD during both feeding periods (backgrounding: R2 = 0.96, P < 0.01; finishing: R2 = 0.98, P < 0.01). The NIRS calibrations for predicting aTTD using the 4-h interval samples or the 4-d–24-h composite were least accurate for NDF and ADF. Most 4-h interval samples could be used to predict eTTD of nutrients, and aside from starch in the finishing period, there were no differences in eTTD using faecal samples collected over 4-h intervals versus those collected over 4 d. Spot faecal samples collected at any time point from multiple cattle have the potential to predict digestibility. Timing of sampling after feeding must be standardized to predict starch digestibility during the finishing period, with samples between 0–4 h and 8–16 h generating estimates of both starch concentration and digestibility that were closest to that derived from 4-d–24-h composite samples.
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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 | 0.000 | 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".