Invited review: Advances and challenges in application of feedomics to improve dairy cow production and health
Bibliographic record
Abstract
Dairy cattle science has evolved greatly over the past century, contributing significantly to the improvement in milk production achieved today. However, a new approach is needed to meet the increasing demand for milk production and address the increased concerns about animal health and welfare. It is now easy to collect and access large and complex data sets consisting of molecular, physiological, and metabolic data as well as animal-level data (such as behavior). This provides new opportunities to better understand the mechanisms regulating cow performance. The recently proposed concept of feedomics could help achieve this goal by increasing our understanding of interactions between the different components or levels and their impact on animal production. Feedomics is an emerging field that integrates a range of omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and metatranscriptomics) to provide these insights. In this way, we can identify the best strategies to improve overall animal productivity, product quality, welfare, and health. This approach can help research communities elucidate the complex interactions among nutrition, environment, management, animal genetics, metabolism, physiology, and the symbiotic microbiota. In this review, we summarize the outcomes of the most recent research on omics in dairy cows and highlight how an integrated feedomics approach could be applied in the future to improve dairy cow production and health. Specifically, we focus on 2 topics: (1) improving milk yield and milk quality, and (2) understanding metabolic physiology in transition dairy cows, which are 2 important challenges faced by the dairy industry worldwide.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".