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Record W4285089895 · doi:10.1139/cjps-2022-0016

Phenotypic evaluation of Canadian × Chinese elite germplasm in a diversity panel for seed yield and seed quality traits

2022· article· en· W4285089895 on OpenAlexaffvenueabout
Chanditha Priyanatha, Istvan Rajcan

Bibliographic record

VenueCanadian Journal of Plant Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGermplasmCultivarBiologyEliteTraitGenetic diversityAgronomyBiotechnologyYield (engineering)HorticulturePopulationMedicinePolitical science

Abstract

fetched live from OpenAlex

Despite mounting concerns regarding the narrowness of the genetic base of soybean (( Glycine max (L.) Merr.) in North America and the challenges that it may pose in the changing global environment and climate, exotic germplasm remains seldom used by breeders owing to various concerns. The objective of this study was to evaluate a Genome-Wide Association Study (GWAS) genomic diversity panel of 200 soybean genotypes for seed yield, seed quality, and agronomic trait performance. The GWAS panel consisted of lines derived from several generations of bi-parental crosses between elite Canadian and elite Chinese cultivars (CD–CH), elite Canadian cultivars (CD), and exotic elite Chinese cultivars (CH) evaluated at Elora and Woodstock, ON, in 2019 and 2020. In the combined analysis of variance, the CD–CH group showed a significant increase in seed yield, although the performance of this group was otherwise comparable or inferior to the adapted elite Canadian cultivars. Canadian cultivars were superior to both CD–CH and elite, exotic Chinese cultivars in seed oil and seed protein concentration. The yield potential of the exotic-derived soybean lines observed in this study provide a great source of novel genetics for soybean breeders interested in introgressing novel alleles from exotic sources to improve yield to help combat climate change.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.096
GPT teacher head0.253
Teacher spread0.156 · 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 designObservational
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

Citations2
Published2022
Admission routes3
Has abstractyes

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