AWARD LECTURE / CONFÉRENCE D'HONNEUR Prospects for controlling plant fungal diseases Alternatives based on chemical ecology and biotechnology
Bibliographic record
Abstract
Plants produce a diverse array of secondary metabolites associated with important defence and resistance mechanisms. In general, within the same plant family the chemical structures of these metabolites are related and suggest common biogenetic precursors. Crucifers are known to produce constitutive and induced tryptophan derived metabolites. Pathogenic fungi can resist such defences utilizing a variety of processes, as for example, enzymatic detoxification. On the other hand, to facilitate their penetration and colonization of the plant tissues, fungal phytopathogens can produce phytotoxic metabolites, some of which are selectively toxic to host-plants. However, plants may be resistant to these pathogens partly due to their ability to detoxify these selective phytotoxins. Nonetheless, the outcome of these interactions frequently favours the pathogen and can lead to enormous yield losses or even major plant epidemics. An overview of some of the most recent studies of metabolic pathways and stress responses in crucifers and several of their pathogens is presented. Potential strategies to prevent and control plant microbial diseases based on chemical ecology studies and biotechnology will be discussed. Key words: phytoalexin, phytotoxin, chemical defence, metabolic detoxification, destruxin B.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.198 | 0.063 |
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".