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Record W3181734238 · doi:10.1017/s147926212100006x

Genetic diversity and GWAS of agronomic traits using an ICARDA lentil (<i>Lens culinaris</i> Medik.) Reference Plus collection

2021· article· en· W3181734238 on OpenAlex
Karthika Rajendran, Clarice J. Coyne, Ping Zheng, Gopesh C. Saha, Dorrie Main, Md. Nurul Amin, Yu Ma, T. J. Kisha, Kirstin E. Bett, Rebecca J. McGee, Shiv Kumar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlant Genetic Resources · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsUniversity of Saskatchewan
FundersAgricultural Research ServiceNational Institutes of HealthUnited States Agency for International DevelopmentU.S. Department of Agriculture
KeywordsBiologyGenome-wide association studyGenetic diversityGenotypingGeneticsGenetic associationBiotechnologySingle-nucleotide polymorphismPopulationEvolutionary biologyGenotypeGene

Abstract

fetched live from OpenAlex

Abstract Genotyping of lentil plant genetic resources holds the promise to increase the identification and utilization of useful genetic diversity for crop improvement. The International Center for Agriculture Research in the Dry Areas (ICARDA) lentil reference set plus collection of 176 accessions was genotyped using genotyping-by-sequencing (GBS) and 22,555 SNPs were identified. The population structure was investigated using Bayesian analysis (STRUCTURE, k = 3) and principal component analysis. The two methods are in concordance. Genome-wide association analysis (GWAS) using the filtered SNP set and ICARDA historical phenotypic data discovered putative markers for several agronomic traits including days to first flower, seeds per pod, seed weight and days to maturity. The genetic and genomic resources developed and utilized in this study are available to the research community interested in exploring the ICARDA reference set plus collection using GWAS.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.496

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 studies0.0010.000
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 head0.048
GPT teacher head0.195
Teacher spread0.147 · 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