Structural specificity in plant–filamentous pathogen interactions
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
Plant diseases bear names such as leaf blights, root rots, sheath blights, tuber scabs, and stem cankers, indicating that symptoms occur preferentially on specific parts of host plants. Accordingly, many plant pathogens are specialized to infect and cause disease in specific tissues and organs. Conversely, others are able to infect a range of tissues, albeit often disease symptoms fluctuate in different organs infected by the same pathogen. The structural specificity of a pathogen defines the degree to which it is reliant on a given tissue, organ, or host developmental stage. It is influenced by both the microbe and the host but the processes shaping it are not well established. Here we review the current status on structural specificity of plant-filamentous pathogen interactions and highlight important research questions. Notably, this review addresses how constitutive defence and induced immunity as well as virulence processes vary across plant organs, tissues, and even cells. A better understanding of the mechanisms underlying structural specificity will aid targeted approaches for plant health, for instance by considering the variation in the nature and the amplitude of defence responses across distinct plant organs and tissues when performing selective breeding.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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