Growth and Development Symposium: Fetal programming in animal agriculture1
Bibliographic record
Abstract
The ability to improve animal production and well-being by altering the maternal environment holds enormous challenges and great opportunities for researchers and animal industries. The concept known as fetal programming, developmental programming, or fetal developmental programming is not a new one. The theory was originally developed using epidemiological data from humans, which showed that in utero and postnatal environmental experiences (i.e., nutrition, disease, and stress) during critical times of early development can have profound long-term effects on development, growth, and disease risk (Barker, 1995). However, animal agriculture has been slow to fully embrace the concept of fetal programming to improve animal growth, development, and well-being. Thus, the joint Annual Meeting of the American Society of Animal Science, the American Dairy Science Association, and the Canadian Society of Animal Science, held in Montreal, Québec, Canada, on July 12 to 16, 2009, provided the ideal forum for a symposium aimed at providing an overview of current knowledge of fetal programming in relation to the animal sciences. Particular emphasis was placed on muscle tissue, milk supply, and reproduction across agriculturally important species.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".