The effect of maternal nutritional restriction on fetal development and performance of offspring in beef cattle
Bibliographic record
Abstract
Health, growth efficiency and carcass quality are major considerations in beef production. The foundation for this is thought to be linked to fetal development. Maternal nutrition during pregnancy is one factor that can influence fetal development and offspring growth. However, its specific effects on postnatal growth of castrated calves (steers) raised for meat are not well understood. This review considers the nutritional requirements of pregnant cows and summarizes the effect of maternal nutritional restriction throughout gestation on calf growth. It also evaluates the different developmental stage at which nutritional restriction will have the greatest impact. It is found that even though early gestation is a critical period for the formation of the placenta and the initial organ development, the fetus has the ability to compensate for developmental restrictions resulting from maternal malnutrition during this period. Meanwhile, during mid-to late gestation maternal nutritional restriction has the greatest impact on offspring performance. Maternal malnutrition during this key period can reduce birth weight, which is strongly correlated to the lifetime performance of steers in terms of health and growth efficiency. Proper nutritional management of cows is essential throughout gestation, but additional attention should be given to the mid-to-late gestational period to ensure calves are born at optimal birth weight. Further, it is suggested that maternal nutrition does not appear to have a consistent effect on carcass quality, instead, external factors near the time of slaughter may be the major determinants.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".