Bibliographic record
Abstract
The importance of antibiotic production for the control of many plant pathogens has been proven for several biocontrol agents.However, the exact mechanisms through which antibiotics operate in order to reduce disease are seldom investigated, and are often assumed to suppress plant pathogen populations through toxicity.Moreover, recent studies have shown that the effect of antibiotics on pathogens is dose-dependent, and that at low, subinhibitory concentrations -which are thought to be prevalent under soil conditions -transcriptional activity rather than viability is affected.Experimental results where the biocontrol of the common scab of potato pathogen, Streptomyces scabies, was achieved in controlled and field trials through the down-regulation of expression of virulence genes, relying on the presence of a phenazine, will be presented as a case study.A literature overview of the fate of antibiotics in soil and their potential roles in the ecosystem will also validate the importance of mechanistically understanding how beneficial microorganisms can achieve disease reduction through antibiosis.This wider understanding will allow the identification and optimization of the conditions necessary for successful disease reduction, and help us anticipate the broader effects of treatments on agroecosystems and their sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.587 | 0.407 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".