Review: Mammary development in swine: effects of hormonal status, nutrition and management
Bibliographic record
Abstract
Farmer, C. 2013. Review: Mammary development in swine: effects of hormonal status, nutrition and management. Can. J. Anim. Sci. 93: 1-7. There are three phases of rapid mammary accretion in swine, namely, from 90 d of age until puberty, during the last third of gestation and throughout lactation. Nutrition, endocrine status and management of gilts or sows during those periods can affect mammary development. More specifically, in growing gilts, feed restriction as of 90 d of age hinders mammary development and either supplying the phytoestrogen genistein or increasing circulating concentrations of prolactin stimulates mammogenesis. In late gestation, inhibition of relaxin or prolactin drastically diminishes mammary development and overly increasing dietary energy has a detrimental effect on mammogenesis. It also appears that feeding of the gestating sow can affect the mammary development of her offspring once it reaches puberty. Various management factors such as litter size, nursing intensity and use or non-use of a teat in the previous lactation will affect the amount of mammary tissue present at the end of lactation. Mammary development is followed by the essential process of involution whereby a rapid and drastic regression in parenchymal tissue takes place. It can occur either after weaning or in early lactation when teats are not being regularly suckled. Despite our current knowledge, much remains to be learned in order to develop the best management strategies for replacement gilts, and gestating and lactating sows that will maximize their milk production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".