Digestibility and growth performance of feedlot cattle fed pelleted grain screenings
Bibliographic record
Abstract
The impact of two different grain screening pellets (GSP) on the chemical profile of feces, feed digestibility, growth performance, and carcass traits of feedlot heifers was measured. Near-infrared spectroscopy (NIRS) was used to predict differences in the chemical composition and energy content of GSP and feces. Heifers (445 ± 35.5 kg) were allocated to 15 pens (10 heifers pen−1) and offered three diets: (1) 76% barley grain (dry matter basis; control); (2) light screening pellets (LSP); and (3) heavy screening pellets (HSP), where GSP replaced 20% barley grain. In controls, fecal starch tended to be higher (P = 0.09) and neutral detergent fiber (NDF) lower (P < 0.01) than heifers fed GSP diets. Fecal nitrogen (N) and ether extract (EE) were also higher (P < 0.05) in heifers fed the control compared with GSP diets. The average daily gain (ADG) of heifers fed LSP tended to be lower (P < 0.06) than the control diet. Gain:feed in controls was higher (P < 0.02) than in those fed GSP diets. Both ADG and G:F were positively associated with fecal N and starch, but negatively associated with NDF. Compared with direct measurements, NIRS over predicted total digestible nutrient (TDN) content of GSP, but did predict most differences in chemical composition.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".