Growth-hormone-releasing factor given to early-pregnant Genex-Meishan and Large White gilts: Effects on growth, carcass, meat quality and histochemical traits of the progeny
Bibliographic record
Abstract
The objective of this study was to determine the effects of growth hormone-releasing factor (GRF) given in early gestation on post-weaning performance, carcass and meat quality and histochemical properties of two pig genotypes. Large White (LW, n = 10) and Genex Meishan-derived dam line (GM, with 50% Meishan genes, n = 10) gilts were treated during gestation with either saline injections (control, n = 5 LW and 5 GM), or 6.6 µg kg-1 of a GRF analog (n = 5 LW and 5 GM), given thrice daily from days 18 to 33 of gestation. After birth, at 56 d of age, four piglets (two barrows and two gilts) each from 20 litters were selected, allotted into individual pens and grown to slaughter weight (108.2 ± 2.3 kg). Feed intake was measured daily and pigs were weighed weekly. Prenatal GRF treatment had a detrimental effect (P < 0.05) on daily gain in both genotypes, but did not affect carcass quality. Significant interactions between GRF, genotype and sex (P < 0.01) for colour traits of the longissimus (L) and semimembranosus (SM) muscles and between GRF and genotype (P < 0.01) for shear force of the L muscle were found. GM pigs had lower growth rate (P < 0.01), higher feed intake (P < 0.05), fatter and shorter carcass (P < 0.001) than LW. L and SM muscles from GM pigs were less exudative (P < 0.05) than LW. L muscle from LW had higher percentages of slow oxidative (SO) (P < 0.001) and fast oxidative glycolytic (FOG) (P < 0.01) fibres but lower percentage of fast glycolytic (FG) fibres (P < 0.001) than that of GM. The results suggest that GRF given in early lactation reduces post-weaning growth of pigs from either breed, but does not affect carcass quality. GM pigs have poorer carcass quality than LW. Key words: Gestating gilts, growth hormone releasing factor, growth, carcass characteristics, meat quality traits, muscle fibre, pig
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".