Order among chaos: high throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system
Bibliographic record
Abstract
ABSTRACT While microbiomes provide diverse benefits for their host, they are notoriously variable in structure and function. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between microbiota, the host and the environment. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, microbiota, and pathogen variables predict host health. Using an Arabidopsis- Pseudomonas host-microbiome-pathogen model, we found that host genotype and order of arrival predict competition outcome between strains in the rhizosphere, but pathogen and microbiota dose can overwhelm these effects. Regardless of host or inoculation conditions, final microbial community structure emerged as a consistent predictor of host health. We demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host health. Increasing the scale at which we can screen components of microbiome-mediated host benefits will facilitate building microbiome engineering solutions for medicine and agricultural applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".