Construction of 2 intraspecific linkage maps and identification of resistance QTLs for<i>Phytophthora capsici</i>root-rot and foliar-blight diseases of pepper (<i>Capsicum</i><i>annuum</i>L.)
Bibliographic record
Abstract
Two linkage maps of pepper were constructed and used to identify quantitative trait loci (QTLs) conferring resistance to Phytophthora capsici. Inoculations were done with 7 isolates: 3 from Taiwan, 3 from California, and 1 from New Mexico. The first map was constructed from a set of recombinant inbred lines (RILs) of the PSP-11 (susceptible) x PI201234 (resistant) cross; and the second map was from a set of F(2) lines of the Joe E. Parker' (susceptible) x 'Criollo de Morelos 334' (resistant) cross. The RIL map covered 1466.1 cM of the pepper genome, and it consisted of 144 markers -- 91 amplified fragment length polymorphisms (AFLPs), 34 random amplified polymorphic DNA (RAPDs), 15 simple sequence repeats (SSRs), 1 sequence characterized amplified region (SCAR), and 3 morphological markers -- distributed over 17 linkage groups. The morphological markers mapped on this population were erect fruit habit (up), elongated fruit shape (fs(e)), and fasciculate fruit clusters (fa). The F(2) map consisted of 113 markers (51 AFLPs, 45 RAPDs, 14 SSRs, and 3 SCARs) distributed in 16 linkage groups, covering a total of 1089.2 cM of the pepper genome. Resistance to both root rot and foliar blight were evaluated in the RIL population using the 3 Taiwan isolates; the remaining isolates were used for the root-rot test only. Sixteen chromosomal regions of the RIL map contained single QTLs or clusters of resistance QTLs that had an effect on root rot and (or) foliar blight, revealing a complex set of genetics involved in resistance to P. capsici. Five QTLs were detected in the F(2) map that had an effect on resistance to root rot.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".