Exploring Diverse Approaches to Iterative Microbiome Passaging in Soil and Plant Systems
Bibliographic record
Abstract
Efficient use of managed land depends on our ability to optimize relevant processes (e.g., crop growth) in that space. Microbial activities are critical to this goal, given their enormous contributions to biogeochemical flux and organismal health. Unfortunately, we still cannot predictably harness their potential in the same way that we can introduce nutrients or manipulate plant composition, for example. In recent years, iterative microbiome passaging has been investigated as an approach for capturing and optimizing groups of microorganisms that contribute additively to functions of interest, such as plant growth promotion or litter decomposition. Early trials show that this approach can alter microbiome function, but functional gains can seem almost stochastic, unlike archetypes of breeding within individual lineages. In this Perspective, we highlight the importance of continuing to explore diverse approaches to iterative microbiome passaging in soil and plant systems, given our limited knowledge about how this process works. There is no single “best” approach, but experimental design choices can have large impacts on outcomes. Ultimately, we believe that a better understanding of different forms of iterative microbiome passaging will allow us to (i) leverage the power of uncultivated microbes, additive/synergistic microbial contributions, and intermicrobial interactions and (ii) understand how land use choices will shape the functional trajectories of microbiomes through time. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".