Comparative study on the relations between backfat thickness in late-pregnant gilts, mammary development and piglet growth1
Bibliographic record
Abstract
Abstract The potential relation between body condition of gilts in late-pregnancy and litter BW gain as well as mammary development was studied using 2 sets of data. Gilts either from a commercial herd (Part 1, n = 182) or from a series of trials looking at mammary development (Part 2, n = 172) were separated in 3 groups according to backfat thickness (BF) on d 110 of gestation. Group categorization was similar for Parts 1 and 2 of the study and was: low (LOW), 13.6 ± 1.6 mm (mean ± SD); medium (MED), 17.6 ± 1.0 mm (mean ± SD); and high BF (HIGH), 21.8 ± 1.8 mm (mean ± SD) for Part 1, and LOW, 14.2 ± 1.3 mm (mean ± SD); MED, 18.1 ± 1.0 mm (mean ± SD), and HIGH 23.4 ± 2.6 mm (mean ± SD) for Part 2. The effects of BF group on piglet BW gain (Part 1) or on various mammary gland characteristics (Part 2) were determined using ANOVA. Litters from HIGH sows tended to have a greater lactation BW gain than those from LOW sows (P < 0.10). Sows with HIGH BF had more mammary parenchymal tissue and more total protein and total DNA than MED and LOW sows (P < 0.05), which led to greater total protein and total DNA contents (P < 0.05). There were strong positive correlations (P < 0.0001) between parenchymal weight and total protein, total DNA, and total RNA. Results suggest that it is beneficial for primiparous sows to have greater BF (i.e., 20 to 26 mm) at the end of gestation to achieve optimal mammary development and greater litter BW gain in the subsequent lactation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".