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Record W2092797191 · doi:10.2135/cropsci2005.0123

Selection for Lodging Resistance in Early Generations of Field Pea by Molecular Markers

2006· article· en· W2092797191 on OpenAlexafffund
Chunzhen Zhang, Bunyamin Tar’an, A. Tullu, Kirstin E. Bett, Albert Vandenberg, Daryl J. Somers

Bibliographic record

VenueCrop Science · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersSaskatchewan Pulse Growers
KeywordsBiologyMarker-assisted selectionSativumSelection (genetic algorithm)Field peaPisumPopulationResistance (ecology)Genetic markerGenomic selectionBreeding programBiotechnologyPlant breedingAgronomyGeneticsBotanyGenotypeGeneCultivarSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Lodging resistance is a key objective in pea breeding programs. Implementation of marker‐assisted selection (MAS) in early generations could significantly enhance the efficiency of the breeding process compared with conventional selection in the F 3 or later generations. The objective of this research was to evaluate the effectiveness of MAS for lodging resistance using a combination of a coupling‐phase linked marker A001 and a repulsion‐phase linked marker A004 in F 2 generation field pea ( Pisum sativum L.). Eight F 2 populations consisting of 680 plants were scored for the markers. A total of 402 F 3 families derived from MAS and 187 F 3 families from unselected populations were evaluated for lodging reaction under field conditions. The lowest lodging scores for each population were obtained from plants with the combination of A001 marker presence and A004 marker absence. A higher proportion of lodging resistant F 3 families was obtained from this marker combination as compared with phenotypic selection in the F 3 generation. MAS was less expensive than phenotypic selection in the field. Thus, A001 and A004 are useful for MAS for lodging resistance in early generation pea breeding populations.

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 score0.583
Threshold uncertainty score0.147

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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

Citations27
Published2006
Admission routes2
Has abstractyes

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