244 The effect of a high-fiber feeding program for replacement gilts on body weight and composition at breeding
Bibliographic record
Abstract
Abstract Sixty-three gilts were recruited at 90 days of age to evaluate the effects of a high-fiber gilt development feeding program on body weight and composition at breeding. Gilts (initial BW 50.9 ± 0.9 kg) were housed individually and randomly attributed to one of four feeding programs: [1] commercial diet fed ad libitum (CON), [2] commercial diet fed 10%, or [3] 20% below ad libitum, and [4] a high-fiber diet fed ad libitum (25% more fiber than the commercial diet and energy density reduced by 5%; FIB). Gilts received the feeding program between 90 days of age and breeding at ~190 days of age. Body weight and feed disappearance were determined weekly. Backfat depth was determined at 90, 145 (puberty), 160, and 190 (breeding) days of age. Over the entire experimental period, CON and FIB gilts had greater ADFI (mean of 3.50 ±0.07 kg) compared to 10% (2.95 ± 0.06 kg) and 20% gilts (2.70 ± 0.07kg; P < 0.05). The FIB feeding program reduced total energy and lysine intakes to amounts similar to 10%; both intakes were less than CON but greater than 20% gilts (P < 0.05). The ADG of FIB was less between days 145 (puberty) and 160 of age compared to CON gilts (0.86 vs. 1.09 ± 0.07 kg; P < 0.05). At breeding, FIB and 10% weighed less (146.5 ± 1.6 kg) than CON (152.7 ± 1.6 kg) and more than 20% gilts (138.7 ± 1.5 kg; P < 0.05). The FIB had less backfat than CON at breeding (14.9 vs. 16.7 ± 0.5 mm; P < 0.05), but did not differ from 10% or 20% gilts. In conclusion, the FIB feeding program limited energy intake, growth, and body fatness of gilts at breeding, even though gilts were offered feed ad libitum. Therefore, high-fiber feeding programs could be a practical means to control growth rates of developing gilts in commercial scenarios.
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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".