Molecular breeding approaches for enhanced resistance against fungal pathogens.
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
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'.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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