Short-Term Eating Preference of Beef Cattle Fed High Forage or High Grain Diets Supplemented with 3-Nitrooxypropanol
Bibliographic record
Abstract
Two experiments were conducted to examine eating preference of beef cattle for diets with or without the investigative enteric methane inhibitor 3-nitrooxypropanol (3-NOP). Nine beef steers were housed in individual stalls, each equipped with two feed bunks. The first experiment (Exp. 1) was conducted with a high forage diet and each animal received a diet without 3-NOP (CON) in one bunk and a diet with 3-NOP (dNOP) in the other bunk. The second study (Exp. 2) was conducted with the same animals about 6 months after Exp. 1 where a high grain diet without (CON) or with 3-NOP (dNOP) was offered. In Exp. 1, animals initially preferred CON compared with dNOP. Feed consumption from 0 to 3, 3 to 6, and 6 to 12 h after feeding was lower for dNOP compared with CON. However, dry matter intake (DMI) and feed consumption of dNOP gradually increased during Exp. 1 such that there was no preference between CON and dNOP on day 7. In Exp. 2, there was no preference for or against dNOP. Average DMI was greater for dNOP vs. CON, but interactions between diet and day for DMI and feed consumption rates indicated that daily preference between CON and dNOP was variable. In conclusion, beef steers initially detected a difference between CON and dNOP and selected in favor of CON rather than dNOP when they had not previously been exposed to 3-NOP. However, the animals rapidly acclimatized to a diet with 3-NOP (Exp. 1) and showed no eating preference between CON and dNOP within 7 days. This lack of preference was maintained throughout Exp. 2 when the same animals were fed a high grain diet.
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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) | 0.001 | 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 | 0.001 |
| 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".