Techniques for screening Cercospora leaf spot resistant fenugreek genotypes
Bibliographic record
Abstract
Cercospora leaf spot (CLS) caused by Cercospora traversiana is an important phyto-pathological problem of fenugreek (Trigonella foenum-graecum L.), a multiuse legume crop. Field screenings for resistant plants, although accurate and effective, demand significant time and a sizable workforce to accomplish. Also, weather conditions in the field may not always be favourable for uniform disease spread, which eventually may lead to failure of the overall experiment. Whole-plant assays (WPA) and detached leaf assays (DLA) with artificial inoculation not only help in scaling up the number of plants screened but also reduce the space, time, and amount of inoculum needed for the experiment. The results from our two experiments indicate that both the WPA and DLA methods can be used reliably to differentiate resistant and susceptible genotypes of fenugreek. In addition, the correlation coefficient between WPA and DLA (r = 0.875, P < 0.01), derived from the mean disease score of each genotype, shows that they can be used interchangeably while screening fenugreek for CLS. DLA was found to be temperature-sensitive for the development of CLS symptoms and wounded leaves developed symptoms faster than non-wounded leaves. These indoor methods can be used for the development of CLS-resistant fenugreek cultivars in areas where disease development is difficult under field conditions.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".