The influence of litter birth weight phenotype on embryonic and placental development at day 30 of gestation in multiparous purebred Large White sows
Bibliographic record
Abstract
The aim of this study was to understand the intrauterine biological processes associated with the low litter birth weight phenotype in pigs. Analyses were conducted on reproductive data from a purebred Large White maternal line to identify sows (>2 parities) with repeatable high or low litter birth weight phenotype (HLBWP or LLBWP). A total of 40 sows were selected (n = 20 HLBWP and n = 20 LLBWP) and bred with semen from purebred Large White boars of proven fertility. Sows were euthanized on day 28-30 of gestation (day 29.5 ± 0.6) and samples of placenta and embryos collected. Total number of embryos (TNE), embryonic weight (EW), embryonic viability, and crown-rump (CRL) measurements were recorded, along with the ovulation rate (OR) and allantochorionic fluid volume (AFV). No significant difference was detected (P > 0.05) in OR, TNE, and number of viable embryos on day 30 of gestation between the two groups. There was no significant difference in EW (LLBWP: 0.80 ± 0.05 g; HLBWP: 0.88 ± 0.04 g, P = 0.18) or CRL (LLBWP: 21.5 ± 0.7 mm; HLBWP: 21.9 ± 0.68 mm, P = 0.46). Placental development represented by the average AFV was significantly lower in the LLBWP compared to HLBWP (LLBWP: 131 ± 9.82 mL; HLBWP: 149 ± 9.39 mL, P = 0.03). In conclusion, placental development may be the main factor causing lower BW of entire litters in LLBWP sows.
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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".