Informal nutrition symposium: leveraging the microbiome (and the metabolome) for poultry production
Bibliographic record
Abstract
Knowledge of gut microbiology of poultry has advanced from a limited ability to culture relatively few microbial species, to attempting to understand the complex interactions between the bird and its microbiome. The Informal Nutrition Symposium 2021 was intended to help poultry scientists to make sense of the implications of the vast amounts of information being generated by researchers. This paper represents a compilation of the talks given at the symposium by leading international researchers in this field. The symposium began with an overview of the historical developments in the field of intestinal microbiology and microbiome research in poultry. Next, the systemic effects of the microbiome on health in the context of the interplay between the intestinal microbiota and the immune system were presented. Because the microbiome and the host communicate and influence each other, the novel field of kinomics (the study of protein phosphorylation) as used in the study of the poultry microbiome was discussed. Protein phosphorylation is a rapid response to the complex of signals among the microbiome, intestinal lumen metabolites, and the host. Then, a description of why an understanding of the role of microbial endocrinology in poultry production can lead to new understanding of the mechanisms by which the gut microbiota and the host can interact in defined mechanisms that ultimately determine health, pathogenesis of infectious disease, and behavior was given. Finally, a view forward was presented underscoring the importance of understanding mechanisms in microbiomes in other organ systems and other species. Additionally, the importance of the development of new -omics platforms and data management tools to more completely understand host microbiomes was stressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".