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Record W2019169876 · doi:10.1139/g05-051

Development and validation of molecular markers linked to an<i>Aegilops umbellulata</i>–derived leaf-rust-resistance gene,<i>Lr9</i>, for marker-assisted selection in bread wheat

2005· article· en· W2019169876 on OpenAlexvenueno aff
Sudhir Kumar Gupta, Ashwini M. Charpe, Sunita Koul, K. V. Prabhu, Qazi Mohd Rizwanul Haq

Bibliographic record

VenueGenome · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersIndian Agricultural Research Institute
KeywordsWheat leaf rustPuccinia reconditaRAPDGeneBiologyPopulationLocus (genetics)Rust (programming language)Marker-assisted selectionGeneticsMolecular markerAegilopsGenetic markerVirulencePloidyMedicine

Abstract

fetched live from OpenAlex

An Aegilops umbellulata-derived leaf-rust-resistance gene, Lr9, was tagged with 3 random amplified polymorphic DNA (RAPD) markers, which mapped within 1.8 cM of gene Lr9 located on chromosome 6BL of wheat. The markers were identified in an F2 population segregating for leaf-rust resistance, which was generated from a cross between 2 near-isogenic lines that differed in the alien gene Lr9 in a widely adopted agronomic background of cultivar 'HD 2329'. Disease phenotyping was done in controlled environmental conditions by inoculating the population with the most virulent pathotype, 121 R63-1 of Puccinia triticina. One RAPD marker, S5550, located at a distance of 0.8+/-0.008 cM from the Lr9 locus, was converted to sequence-characterized amplified region (SCAR) marker SCS5550. The SCAR marker was validated for its specificity to gene Lr9 against 44 of the 50 known Lr genes and 10 wheat cultivars possessing the gene Lr9. Marker SCS5550 was used with another SCAR marker, SCS73719, previously identified as being linked to gene Lr24 on a segregating F2 population to select for genes Lr9 and Lr24, respectively, demonstrating the utility of the 2 markers in marker-assisted gene pyramiding for leaf-rust resistance in wheat.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.225
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations83
Published2005
Admission routes1
Has abstractyes

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