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Record W2001318047 · doi:10.4141/cjps09128

Gamete and recurrent selection for improving physiological resistance to white mold in common bean

2010· article· en· W2001318047 on OpenAlexvenueno aff
Henry Terán, Shweta Singh

Bibliographic record

VenueCanadian Journal of Plant Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSclerotinia sclerotiorumPhaseolusBiologyHorticultureWhite (mutation)Selection (genetic algorithm)InoculationAgronomyGeneticsGene

Abstract

fetched live from OpenAlex

White mold (WM) is a widely distributed and severe disease of common bean in North America. The objective of this study was to determine the effectiveness of gamete (GS) and recurrent (RS) selection methods for improving WM resistance. Two double-crosses, namely USPT-WM-1/CORNELL 601//USPT-CBB-1/92BG-7 and ‘Chas’/I 9365-25//ABL 15/A 195 were made. Equal numbers of progenies from both crosses were subjected to GS and RS. The GS was practiced from F 1 to F 4 by selecting WM resistant single plants followed by progeny testing in the subsequent generation. Two cycles of RS were practiced by intermating selected WM resistant plants in each cycle. Thirteen selected families in each method and parents were evaluated at 16, 23, and 33 days post first inoculation in replicated trials in two greenhouse environments. Higher frequencies of families with lower WM scores were obtained from GS than RS. The average genetic gains due to GS and RS were 19.6 and 7.9%, respectively. Employing multiple-parent-crosses involving parents of diverse evolutionary origins delayed WM evaluation, and application of GS are recommended for improving physiological WM resistance in common bean.Key words: Interspecific breeding line, introgressing resistance, multiple-parent crosses, Phaseolus vulgaris, pyramiding resistance, Sclerotinia sclerotiorum

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)0.001
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 score0.958
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations20
Published2010
Admission routes1
Has abstractyes

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