Prairie Agroecosystems: Interconnected Microbiomes of Livestock, Soil and Insects
Bibliographic record
Abstract
Agroecosystems are comprised of environmental compartments where associated microbial communities interact with one another. These microbial communities, called microbiomes, inhabit livestock, insects, and plants. Microbiomes are also present in the soil and watersheds. Clarifying the nature and extent of microbial interactions between compartments both at intra-farm and global scales can promote sustainable production systems, healthier animals, increased crop yields, and safer meat products. Early research on microbiomes was hindered by a lack of expertise and the high cost of molecular sequencing. However, these limitations have been largely resolved with advances in and reduced costs of sequencing technologies. In this paper, we summarize sequencing and bioinformatics approaches, and review the crucial roles of diverse microbiomes in livestock, plants and soil, as well as pollinators and pest insects. These crucial roles include nutrient cycling, nutrient acquisition, metabolism of toxins and enhanced host immune function. Additionally, we examine potentially undesirable effects of microbiomes associated with climate change and agri-food production such as their role in the release of greenhouse gases from cattle and their impact on meat safety and spoilage. By increasing the awareness of microbiomes and the growing ease with which they can be studied, we hope to foster a greater adoption of microbiome research. Further understanding of the diverse effects and interactions of microbiomes will advance our efforts to increase agricultural production while reducing its negative environmental footprint, thus making the agroecosystems more sustainable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".