Molecular breeding approaches for enhanced resistance against fungal pathogens.
Bibliographic record
Abstract
Marker-assisted selection for fungal plant resistance is the most important tool in molecular breeding at the applied level. Markers for disease resistance have been sought by researchers and breeders since the discovery that genes can be linked to each other. The dearth of visual markers has been the limiting factor in their application, but that has changed with the development of techniques to detect variation in DNA. The differences in DNA are visualized as polymorphisms which currently are predominantly identified as changes in fragment size, made possible through techniques such as polymerase chain reaction, electrophoresis, fluorescent dye detection and the use of restriction enzymes. Because of the many examples of monogenic inheritance of disease resistance genes and the importance of resistance traits, the processes of marker discovery have developed in large part around disease resistance. There are now a vast number of markers for the many resistance genes to fungal diseases in numerous crop species. The integration and use of these markers takes breeding from integrating the technology in marker-assisted selection to the development of breeding strategies around marker use in 'molecular breeding'.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".