Short Communication: Relations between peripartum concentrations of prolactin and progesterone in sows and piglet growth in early lactation
Bibliographic record
Abstract
Quesnel, H., Ramaekers, P., van Hees, H. and Farmer, C. 2013. Short Communication: Relations between peripartum concentrations of prolactin and progesterone in sows and piglet growth in early lactation. Can. J. Anim. Sci. 93: 109–112. Postpartum hypogalactia has been suggested to be related to an impaired secretion of prolactin or to elevated concentrations of progesterone around farrowing. In the current study, peripartum circulating concentrations of prolactin and progesterone were determined in 50 multiparous sows (parities 1 to 5) and related to the Na K−1 ratio and lactose in colostrum and to piglet growth in early lactation. An effect of parity (P<0.001) was observed for prolactin, with sows from parity 1 having lower concentrations than sows from parities 2, 3, 4 and 5, and sows from parities 4 and 5 having the greatest concentrations. Piglet growth from day 1 to day 5 was negatively correlated with progesterone concentrations in sows on day 1 (r=−0.36, P=0.01). The present study supports the hypothesis of a negative impact of high concentrations of progesterone after farrowing on early growth of piglets and also shows a clear effect of sow parity on the peripartum concentrations of prolactin suggesting that younger sows are more vulnerable to reduced milk yield due to lower prolactin concentrations.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".