Variations on a theme: Non-canonical mechanisms of effector-triggered immunity
Bibliographic record
Abstract
Effector-triggered immunity (ETI) can be defined as immune responses activated upon specific recognition of a pathogen effector protein by its cognate plant immune receptor protein. This classic gene-for-gene model of the interaction of one pathogen effector, also known as an Avirulence (Avr) gene, with one plant immune receptor gene, known as a Resistance (R) gene has been documented since the 1950s. Since then, different types of recognition that deviate from the gene-for-gene model, for example, immune receptor pairs and immune receptor networks, have been identified. In addition, while many R genes encode NLR (nucleotide binding, leucine rich repeat) proteins, R genes that encode only parts of NLR domains, and non-NLR encoding R genes such as tandem kinases have been identified, broadening the immune receptor repertoire in plants. In recent years, there have been significant advances in understanding the molecular mechanisms of NLR intracellular immune receptors in plants, including how they are inhibited, activated, and regulated. This review covers recent developments in ETI initiation mechanisms and in plant NLR biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".