Bibliographic record
Abstract
Sows do not produce enough milk to sustain optimal growth of their litters.This is particularly important when considering the current hyperprolific sow genetic lines.Mammary development needs to be considered to improve potential milk yield.One can only attempt to stimulate mammogenesis during periods when rapid mammary development is already ongoing.There are two such periods before lactation starts, namely, from three months of age until puberty, and from 90 days of gestation until farrowing.Early studies showed that a 20% feed restriction from 90 days of age until puberty drastically reduces mammary parenchymal tissue mass.Yet, in a more recent study, sow milk yield was not altered following a 10% or 20% feed restriction, or a 25% dietary fibre addition from 90 days of age to breeding.This absence of effect was likely due to the greater feed intake of control gilts in that recent study compared with the older studies, and suggested that feed intake of growing gilts can be reduced to 2.7 kg/d (but not 2.1 kg/d) without detrimental effects on future milk yield.During prepuberty, inclusion of the phytoestrogen genistein in the diet increases the number of mammary parenchymal cells.During late gestation, feeding very high energy levels may have detrimental effects on mammary development and subsequent milk production.Feed intake throughout gestation is also important because of its effect on body condition, with gilts that are too thin (< 16 mm backfat thickness) in late gestation showing less mammary development.A 40% increase in lysine intake via inclusion of additional soybean meal to the diet of gilts from days 90 to 110 of gestation increased mammary parenchymal mass by 44%.Increasing circulating concentrations of the growth factor IGF-1 during late gestation also increased mammary parenchymal mass by 22%.Current data clearly demonstrate that feeding management before lactation can be used to enhance mammary development, hence future milk yield.
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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) | 0.002 | 0.001 |
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".