The chemical ecology of crucifers and their fungal pathogens: Boosting plant defenses and inhibiting pathogen invasion
Bibliographic record
Abstract
Fungal plant diseases can cause very substantial yield losses in crucifer crops such as rapeseed and canola, or vegetables such as cabbage and broccoli. To devise sustainable methods to prevent and deter crucifer pathogens, the chemical interaction between crucifers and their fungi is under intense investigation. Crucifers produce complex blends of secondary metabolites with diverse ecological roles that include protection against microbial pathogens and other pests. The secondary metabolites involved in crucifer defense, namely phytoalexins and phytoanticipins, and their metabolism by fungal pathogens indicate that some fungi produce different enzymes to detoxify these metabolites and that some fungal detoxifying enzymes are rather specific. Chemical synthesis and screening of phytoalexin analogue libraries using cultures of fungal pathogens, as well as protein extracts, have shown that such detoxification reactions can be inhibited and that some inhibitors are strongly antifungal. Overall results of current work show the feasibility of using selective inhibitors of fungal detoxifying enzymes, i.e., paldoxins, to protect plants by boosting their chemical defenses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".