161 Effect of High- and low-Fiber Diets on Growth Performance in Growing-Finishing Pigs Selected for low or High Feed Efficiency
Bibliographic record
Abstract
Abstract Understanding the effects of high- and low-fiber diets on the growth performance of pigs with different feed efficiencies will help the livestock industry develop new strategies to reduce the costs of pig production. In this study, 18 Landrace × Large White sows with high and low estimated breeding value on feed conversion ratio (EBV-FCR) were inseminated with semen from Large White with known EBV-FCR to produce 9 litters of low feed efficiency pigs and 9 litters of high feed efficiency pigs. A total of 94 growing pigs with low or high feed efficiency were fed a low-fiber (3% crude fiber) or high-fiber (6% crude fiber) content diet in a 2 × 2 factorial arrangement for 70 days. Pigs fed a high-fiber diet presented higher body weight (BW) on day 70 (P < 0.05). High feed efficiency pigs presented lower average daily feed intake (ADFI) and feed conversion ratio (FCR) than low feed efficiency pigs from day 0 to 70 (P< 0.05). There was an interaction between fiber and feed efficiency group on average daily gain (ADG;P < 0.05) and FCR (P < 0.05) from day 0 to 35. High feed efficiency pigs fed a high-fiber diet presented lower FCR than low feed efficiency pigs fed a low-fiber diet (P< 0.05). Low feed efficiency pigs fed a low-fiber diet showed the lowest ADG (P< 0.05). Regardless of efficiency groups, pigs fed a high-fiber diet presented higher ADG and lower FCR than pigs fed a low-fiber diet from day 42 to 70 (P< 0.05). These results suggest that high feed efficiency pigs can present lower FCR and ADFI without reducing final BW and ADG. Feeding a high-fiber diet can increase ADG and reduce FCR in the later stages of the experiment.
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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".