Plant Innate Immune Response: Qualitative and Quantitative Resistance
Bibliographic record
Abstract
Plant diseases, caused by microbes, threaten world food, feed, and bioproduct security. Plant resistance has not been effectively deployed to improve resistance in plants for lack of understanding of biochemical mechanisms and genetic bedrock of resistance. With the advent of genome sequencing, the forward and reverse genetic approaches have enabled deciphering the riddle of resistance. Invading pathogens produce elicitors and effectors that are recognized by the host membrane-localized receptors, which in turn induce a cascade of downstream regulatory and resistance metabolite and protein biosynthetic genes (R) to produce resistance metabolites and proteins, which reduce pathogen advancement through their antimicrobial and cell wall enforcement properties. The resistance in plants to pathogen attack is expressed as reduced susceptibility, ranging from high susceptibility to hypersensitive response, the shades of gray. The hypersensitive response or cell death is considered as qualitative resistance, while the remainder of the reduced susceptibility is considered as quantitative resistance. The resistance is due to additive effects of several resistance metabolites and proteins, which are produced through a network of several hierarchies of plant R genes. Plants recognize the pathogen elicitors or receptors and then induce downstream genes to eventually produce resistance metabolites and proteins that suppress the pathogen advancement in plant. These resistance genes (R), against qualitative and quantitative resistance, can be identified in germplasm collections and replaced in commercial cultivars, if nonfunctional, based on genome editing to improve plant resistance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".