A Personal Perspective of the Last 40 Years of Plant Pathology: Emerging Themes, Paradigm Shifts and Future Promise
Bibliographic record
Abstract
Abstract The last 40 years of experimental research have resulted in a remarkable increase in our understanding of plant disease resistance to microbial pathogens, with a recent surge of clarity primarily provided by the application of molecular genetics to pathogen interactions withArabidopsis thaliana. Research foci have changed over time with the availability of new techniques and the ability to identify genes, proteins, signalling systems and defensive biochemicals involved in plant resistance. In hindsight, early concepts were generally simplistic. Although some have been supported by subsequent data, others, such as the basis for the gene‐for‐gene phenomenon, have changed dramatically and it is now clear that plant–microbe interactions are sophisticated and complex. Much is left to discover: the role of the hypersensitive response is still enigmatic, the interplay of recognition events and defensive factors that control host or non‐host resistance is still not clear and the number of well‐studied pathosystems is still few. The future promises more attention to the spatial organisation of disease resistance at the cellular level, and new insights into the evolution of disease resistance and pathogen pathogenicity. Particularly urgent is the need for unequivocal data to prove which plant genes and processes involved in disease resistance are primarily responsible for the restriction of pathogen growth. Disappointingly, our considerable progress in understanding plant–microbe interactions in the last 40 years has not translated into comparable progress in developing novel, widespread and effective methods of disease control in the field, and this remains a significant challenge for the future.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".