Gestational and lactational feeding strategies for gilts: Growth, carcass characteristics and meat quality of the progeny
Bibliographic record
Abstract
This study was conducted to determine whether feeding gilts (1) at or above their National Academy of Sciences-National Research Council (NAS-NRC 1998) requirements during gestation, and (2) to lose a moderate (~10%) or large (~17%) amount of maternal protein during lactation had a residual effect on their progeny’s growth, carcass characteristics and pork quality at market weight. From each litter, the heaviest and lightest barrows and gilts were selected. The progeny of gilts fed above their requirements during gestation, and those that lost the least body protein during lactation were heavier at weaning; +0.3 kg (P < 0.05) and +0.5 kg (P = 0.01), respectively. However, these liveweight differences, which were associated with the gestation and lactation effects, were no longer evident (P > 0.05) at day 35 or 85 post-weaning. But at slaughter, these animals had thinner (P < 0.01) fat thickness and higher (P < 0.05) predicted salable meat yield. Independently of the gestation and lactation treatments, and compared to the low-weaning-weight pigs, the high- weaning-weight pigs maintained their weight advantage (P < 0.01 at day 35 (+ 2.8 kg) and day 85 (+ 5.4 kg) post-weaning), took 4.5 fewer days (P < 0.01) to reach market weight, but had similar (P > 0.05) carcass characteristics and pork quality. Key words: Gilts, gestational and lactational protein, litter, growth, carcass characteristics and meat quality
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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 | 0.001 |
| 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".