Predictability of growth performance in feedlot cattle using fecal near infrared spectroscopy
Bibliographic record
Abstract
Near-infrared spectroscopy (NIRS) was used to predict nutrients and apparent total tract digestibility (aTTD) of nutrients and gross energy (GE) using 282 dried ground fecal samples collected monthly over 13 mo from the pen floor of six feedlots in southern Alberta. Mixed-model regression was used to examine relationships among fecal composition, digestibility, dry matter intake (DMI), average daily gain (ADG), and gain to feed ratio (G:F). Lower (P < 0.01) fecal starch, greater (P ≤ 0.04) fecal neutral detergent fiber, and greater (P ≤ 0.01) aTTD of dry matter (DM), organic matter (OM), starch, and GE were observed in cattle fed tempered versus dry-rolled barley, with no differences in DMI, ADG, or G:F. Compared with cattle fed barley, those fed a wheat–barley grain mixture had greater (P ≤ 0.02) fecal starch and aTTD of DM, OM, as well as greater ADG, and G:F. Heifers had a lower (P ≥ 0.05) aTTD of DM and GE than steers. A quadratic relationship was observed between fecal starch and G:F, with sex and average body weight (BW) at time of sampling as additional variables (ρ = 0.75, P < 0.01). Our data indicate that NIRS predictions using the feces of feedlot cattle have potential in predicting G:F when variables such as BW and sex are included in the equation.
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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 | 0.001 | 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".