Effects of Different Levels of Fermented Oat on Growth Performance, Nutrientdigestibility, Diarrhea Incidence, Fecal Microorganisms and Emission Gas in Weaned Pigs
Bibliographic record
Abstract
This study was conducted to investigate the effects of different levels of fermented oat meal on growth performance, diarrhea incidence, fecal microorganisms, and emission gas in weaned pigs. A total of 80 crossbred piglets (7.31 ± 0.24 kg) weaned at 21 days of age were assigned to treatments in a randomized complete block design based on the initial body weight (BW). This experiment included 2 phases. In the first phase (from 0 to 21 d), there were 5 treatments: T1 (15% nature oat), T2 (3.7% fermented oat + 11.3% nature oat), T3 (7.5% fermented oat + 7.5% nature oat), T4 (11.3% fermented oat + 3.7% nature oat), T5 (15% fermented oat). In the second phase (from 21 to 35 d): T1 (7% nature oat), T2 (1.75% fermented oat + 5.25% nature oat), T3 (3.5% fermented oat + 3.5% nature oat), T4 (5.25% fermented oat + 1.75% nature oat), T5 (7% fermented oat). Pigs had access to feed and water ad libitum and their BW and average daily feed intake (ADFI) were measured for each phase throughout the duration of the experiment. During the first phase,the use of 7.5%, 11.3% and 15% fermented oat to replace nature oat improvedaverage daily gain (ADG) and gain/feed ratio (G/F) (P<0.05). . During the second phase, the use of 3.5%, 5.3%, and 7% fermented oat to substitute nature increased ADG. Entire period of this experiment, T3 and T4 treatments were presented higher ADG, G/F compared with T1 and T2 treatments. Different levels of fermented oat didnot affect diarrhea incidence score, digestibility, fecal microorganisms and emission gas. It is concluded thatfermented oat can serve as an alternative feed ingredient for possibly replacing the use of nature oat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".