Plant Associated Rhizobacteria for Biocontrol and Plant Growth Enhancement
Bibliographic record
Abstract
Crop disease remains a major problem to global food production. Excess use of pesticides through chemical disease control measures is a serious problem for sustainable agriculture as we struggle for higher crop productivity. The use of plant growth promoting rhizobacteria (PGPR) is a proven environment friendly way of controlling plant disease and increasing crop yield. PGPR suppress diseases by directly synthesizing pathogen-antagonizing compounds, as well as by triggering plant immune responses. It is possible to identify and develop PGPR that both suppress plant disease and more directly stimulate plant growth, bringing dual benefit. A number of PGPR have been registered for commercial use under greenhouse and field conditions and a large number of strains have been identified and proved as effective biocontrol agents (BCAs) under environmentally controlled conditions. However, there are still a number of challenges before registration, large-scale application, and adoption of PGPR for the pest and disease management. Successful BCAs provide strong theoretical and practical support for application of PGPR in greenhouse production, which ensures the feasibility and efficacy of PGPR for commercial horticulture production. This could be pave the way for widespread use of BCAs in agriculture, including under field conditions, to assist with both disease management and climate change conditions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".