Threonine requirement increases in late pregnancy
Bibliographic record
Abstract
The metabolism in early pregnancy focuses on development of maternal body tissue. In late pregnancy fetal, and mammary growth increases the demand for nutrients. This change in metabolic focus is hypothesized to result in a higher requirement for amino acids during late pregnancy (3 rd trimester) compared to early pregnancy (1 st trimester). The threonine (THR) requirement was determined using the indicator amino acid (IAA) oxidation method in six multiparous sows. Sows received diets ranging from 60 to 150 % of the current recommended THR intake of 10 gd −1 based on BW, expected pregnancy gain and litter size. L[1‐ 13 C]phenylalanine was given orally in 8 ½‐hourly meals as IAA and expired 13 CO 2 was quantified. Data was analyzed with a nonlinear Mixed model. Sow BW gain and reproductive performance was similar to commercial standards. There was a 2 fold difference in THR requirement. The THR requirement in early pregnancy was no more than 6.1 gd −1 (R 2 =0.56) and in late pregnancy no less than 13.6 gd −1 (R 2 =0.59). The THR requirement of the sow is substantially higher in late pregnancy than currently recommended. Nutritional regimes in pregnancy should account for changes in AA requirement to reduce the risk of overfeeding AA in early pregnancy and underfeeding AA in late pregnancy. Funding: AB Pork, ON Pork, AB Livestock Industry Development Fund, ACAAF, Ajinomoto Heartland.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".