Hormonal, behavioural and performance characteristics of Meishan sows during pregnancy and lactation
Bibliographic record
Abstract
Meishan sows are known for their high prolificity and great lactational performances. Specific breed characteristics in terms of their embryonic, foetal and placental developments as well as their differences in mammary development at the end of gestation are covered. The various known metabolic, physiological and endocrine factors related to the decreased embryonic mortality and increased placental vascularity, which are largely responsible for the greater litter size of Meishans, are discussed. An overview of published data on the endocrine status of the sow and foetuses throughout pregnancy is also presented. The superiority of Meishan sows during lactation is described in terms of its various components (i.e., piglet growth and development, sow and litter behaviour, milk composition) and the breed differences pertaining to sow metabolism and endocrinology during lactation are covered in order to provide an insight as to the possible mechanisms responsible for these superior performances. This review illustrates how a better understanding of the biological differences between Meishan sows and sows from European breeds could benefit the development of new management schemes to further improve reproductive potential of sows from traditional breeds. Key words: Meishan, gestation, lactation, hormones, behaviour, performance
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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".