Allele Mining of Major Gall Midge Resistance Genes <em>gm3, Gm4,</em> Gm8 and Gm11 in Selected Sri Lankan Rice Germplasm
Bibliographic record
Abstract
Breeding ri ce varieties carrying resistance to rice gall midge (RGM), Orseolia oryzae is a key strategy to reduce yield losses incurred as a result of RGM infestations, globally. Using associated DNA-markers, the study evaluated 55 Sri Lankan ric e accessions (23 traditional (TAs) and 32 newly improved rice varieties (NIVs)) based on their breeding potential t o identify rice accessions carrying resistance alleles at four major RGM resistance genes: gm3, Gm4, Gm8, and Gm11. The allele profiling revealed that none of the rice accessions carried resistance alleles for all four genes and five access ions carried only susceptible alleles at the target loci. Eleven accessions carried resistance alleles at gene combinations Gm4, Gm8 and Gm11 (7), gm3, Gm8 and Gm11 (3), and gm 3, Gm4 and Gm11 (1). Twenty-four accessions reported combinations of any two resistance alleles from the four target genes, and 15 rice accessions carried only one resistance allele for the three target genes gm3, Gm8 and Gm11. The resistance alleles of Gm11 (56%) and gm3 (49%) were the most common in the study panel, and the resistance allele of Gm 4 was the least prevalent (33%). Considering all four resistance genes, TAs carried the resistance alleles mostly com pared to the NIVs, except in the gene gm3. The RGM resistance allele profiling conducted herewith will facilitate taking informative decisions at parental and donor selection for crosses and gene pyramiding, in rice breeding programs. The study must be further expanded with a field evaluation of rice accessions for resistance to RGM and discovering novel resistance genes in the local germplasm to broaden the understanding of RGM resistance in rice.
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 | 0.001 |
| 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".