The chemical ecology of crucifers and their fungal pathogens: Boosting plant defenses and inhibiting pathogen invasion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it