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Record W2021003000 · doi:10.1080/07060660409507118

Development of sequence-characterized amplified region (SCAR) markers for resistance gene<i>Pc94</i>to crown rust in oat

2004· article· en· W2021003000 on OpenAlexaffvenue
J. Chong, E. Reimer, Daryl J. Somers, Aung Tun Oo, G. A. Penner

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyAvenaRust (programming language)GeneGeneticsPopulationCrown (dentistry)Molecular markerPlant disease resistanceGenetic markerBotanyMedicine

Abstract

fetched live from OpenAlex

Crown rust, caused by Puccinia coronata f. sp. avenae, is an important disease of oat (Avena sativa). Pyramiding effective resistance from diverse sources is one strategy to provide more durable control of crown rust. The objective of this study was to develop a DNA marker for the resistance gene Pc94 to crown rust derived from the diploid oat Avena strigosa. Amplified fragment length polymorphism generated two DNA fragments that were associated with the resistance gene Pc94 in a F2 population derived from a cross between the rust-susceptible oat 'Calibre' and a near-isogenic hexaploid oat line, S42, possessing Pc94. One of the fragments was successfully converted into two sequence-characterized amplified region (SCAR) markers linked to the Pc94 gene in the Calibre/S42 F2 population. Specificity of the SCAR markers to the Pc94 gene was confirmed in three other populations. The estimated distance between the SCAR markers and Pc94 ranged from 0.9 to 3.4 centi-Morgan. These SCAR markers developed for the Pc94 gene will be valuable tools for pyramiding this gene with other resistance genes to crown rust in marker-assisted breeding programs when crown-rust races with the appropriate virulence combinations are not available to detect the genes being introgressed.

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 score0.885
Threshold uncertainty score0.906

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 head0.043
GPT teacher head0.220
Teacher spread0.177 · 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

Citations38
Published2004
Admission routes2
Has abstractyes

Explore more

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