Invited review: “Probiotic” approaches to improving dairy production: Reassessing “magic foo-foo dust”
Bibliographic record
Abstract
The gastrointestinal microbial consortium in dairy cattle is critical to determining the energetic status of the dairy cow from birth through her final lactation. The ruminant's microbial community can degrade a wide variety of feedstuffs, which can affect growth, as well as production rate and efficiency on the farm, but can also affect food safety, animal health, and environmental impacts of dairy production. Gut microbial diversity and density are powerful tools that can be harnessed to benefit both producers and consumers. The incentives in the United States to develop Alternatives to Antibiotics for use in food-animal production have been largely driven by the Veterinary Feed Directive and have led to an increased use of probiotic approaches to alter the gastrointestinal microbial community composition, resulting in improved heifer growth, milk production and efficiency, and animal health. However, the efficacy of direct-fed microbials or probiotics in dairy cattle has been highly variable due to specific microbial ecological factors within the host gut and its native microflora. Interactions (both synergistic and antagonistic) between the microbial ecosystem and the host animal physiology (including epithelial cells, immune system, hormones, enzyme activities, and epigenetics) are critical to understanding why some probiotics work but others do not. Increasing availability of next-generation sequencing approaches provides novel insights into how probiotic approaches change the microbial community composition in the gut that can potentially affect animal health (e.g., diarrhea or scours, gut integrity, foodborne pathogens), as well as animal performance (e.g., growth, reproduction, productivity) and fermentation parameters (e.g., pH, short-chain fatty acids, methane production, and microbial profiles) of cattle. However, it remains clear that all direct-fed microbials are not created equal and their efficacy remains highly variable and dependent on stage of production and farm environment. Collectively, data have demonstrated that probiotic effects are not limited to the simple mechanisms that have been traditionally hypothesized, but instead are part of a complex cascade of microbial ecological and host animal physiological effects that ultimately impact dairy production and profitability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".