Race Characterization of Phytophthora Root Rot on <i>Capsicum</i> in Taiwan as a Basis for Anticipatory Resistance Breeding
Bibliographic record
Abstract
Peppers (Capsicum sp.) are an increasingly important crop because of their use as a vegetable, spice, and food colorant. The oomycete Phytophthora capsici is one of the most devastating pathogens to pepper production worldwide, causing more than $100 million in losses annually. Developing cultivars resistant to P. capsici is challenging because of the many physiological races that exist and new races that are continuously evolving. This problem is confounded by the lack of a universal system of race characterization. As a basis to develop a global anticipatory breeding program, New Mexico recombinant inbred lines (NMRILs) functioned as a host differential for Phytophthora root rot to characterize the race structure of P. capsici populations in Taiwan. Using the NMRILs, 24 new races were identified, illustrating the utility and usefulness of the NMRILs for anticipatory breeding. Virulence of P. capsici was observed to be geographically specific and in two virulence clusters. Interestingly, all but two isolates collected in 2016 were the A2 mating type, which is a shift from the predominantly A1 mating type isolates collected prior to 2008. The NMRILs host differential provides an approach for scientists to work together on a global scale when breeding for resistance as well as on a local level for regional gene deployment. Additionally, we propose that the current race numbering system, which has no biological meaning, be supplemented with the virulence phenotype, based on the susceptible NMRILs to a given isolate. This work provides insights into the population dynamics of P. capsici and interactions within the highly complex Capsicum-Phytophthora pathosystem, and offers a basis for similar research in other crops.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".