GROWTH AND DEVELOPMENT SYMPOSIUM: Intestinal development and growth1
Bibliographic record
Abstract
The gastrointestinal (GI) tract is a critical organ system mediating nutrient uptake and use by the animal. Understanding factors that influence GI development, growth, and function is critical to improving management and therapeutic approaches to maximize health and production efficiency of livestock. These factors are diverse and include animal nutrition and nutrient interactions with the GI tract, animal genetics, and the physical and biological environments of the animal, such as the interactions between the host and its diverse GI microbiome. Recent advances in scientific technology and computing capacity now permit the systematic study of genes and their products, including those derived from the animal GI tract, as well as the complex microbial communities within the GI tract. Thus, the interactions of microbial communities with their host animals and their impacts on animal production and health are increasingly being studied and are becoming better understood. A special case to consider is the development of the GI tract of neonatal livestock and newly hatched poultry. The fetal GI tract is not exposed to microbes prepartum or prehatching, but the microbial population becomes established during parturition or upon first feeding. The colonization of the GI tract is rapid and complex with numerous types of microbes. Increasing evidence suggests that manipulating the diversity of the gut microflora can affect animal health and can be accomplished through changes in production practices. For example, results derived from studies of humans and rodents indicate that the composition of the gut microbiota can influence the efficiency of energy extraction from the diet, as well as the production of inflammatory cytokines in the gut that contribute to obesity and metabolic syndrome (Frank, 2011). Furthermore, the gut microbial population influences nutrient homeostasis including that of AA, vitamins, electrolytes, and short-chain fatty acids (Frank, 2011). Thus, in theory, the diversity of the GI microbiota could be manipulated or optimized to promote animal production and well-being (Cook, 2011). The combination of advances in new technologies and the potential impacts of the interactions that affect GI tract development and, ultimately, animal development, health, production efficiency, and product quality, stimulated the initiation of a symposium to highlight current knowledge of these complex interplays. This symposium was presented at the Joint Annual Meeting of the American Society of Animal Science, the American Dairy Science Association, the Poultry Science Association, the Asociación Mexicana de Producción Animal, and the Canadian Society of Animal Science on July 12, 2010, in Denver, Colorado.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.171 | 0.104 |
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".