Genome exploration and ecological competence are key to developing effective <i>Pseudomonas</i> -based biocontrol inoculants
Bibliographic record
Abstract
Numerous plant-beneficial Pseudomonas strains have been isolated and characterized for their ability to control plant pathogens and the diseases they cause under controlled conditions. Only a few have, however, demonstrated consistent field efficacy. Better exploitation of genomic information and consideration of the ecological competence of strains of interest could help overcome this major inconsistency. In this minireview, we will discuss these two important aspects that we consider crucial in the development of effective Pseudomonas biocontrol inoculants. We will first explore how the increasing availability of genomic data can empower researchers who study Pseudomonas-mediated biocontrol to better understand the mechanisms at play. We will then discuss the key roles played by ecological competence in the successful development of Pseudomonas-based biocontrol inoculants and how researchers can better select ecologically competent strains. A better understanding of these factors could help accelerate the development of effective Pseudomonas inoculants and prevent wasting precious time and resources performing field experiments with strains that have little chance to succeed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".