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Record W2302750813 · doi:10.1534/g3.116.026914

Environmental Association Analyses Identify Candidates for Abiotic Stress Tolerance in<i>Glycine soja</i>, the Wild Progenitor of Cultivated Soybeans

2016· article· en· W2302750813 on OpenAlexaff
Justin Anderson, Thomas J. Y. Kono, Robert M. Stupar, Michael B. Kantar, Peter L. Morrell

Bibliographic record

VenueG3 Genes Genomes Genetics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGermplasmBiologyAdaptation (eye)Abiotic componentAbiotic stressEx situ conservationLocal adaptationPopulationSingle-nucleotide polymorphismSelection (genetic algorithm)Glycine sojaQuantitative trait locusEvolutionary biologyGeneticsBiotechnologyGenotypeGeneGlycineEcologyBotany

Abstract

fetched live from OpenAlex

Natural populations across a species range demonstrate population structure owing to neutral processes such as localized origins of mutations and migration limitations. Selection also acts on a subset of loci, contributing to local adaptation. An understanding of the genetic basis of adaptation to local environmental conditions is a fundamental goal in basic biological research. When applied to crop wild relatives, this same research provides the opportunity to identify adaptive genetic variation that may be used to breed for crops better adapted to novel or changing environments. The present study explores an ex situ conservation collection, the USDA germplasm collection, genotyped at 32,416 SNPs to identify population structure and test for associations with bioclimatic and biophysical variables in Glycine soja, the wild progenitor of Glycine max (soybean). Candidate loci were detected that putatively contribute to adaptation to abiotic stresses. The identification of potentially adaptive variants in this ex situ collection may permit a more targeted use of germplasm collections.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.262

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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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

Citations48
Published2016
Admission routes1
Has abstractyes

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