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Record W2749553092 · doi:10.1111/pbr.12511

Genetic structure of putative heterotic populations of alfalfa

2017· article· en· W2749553092 on OpenAlexaboutno aff
Paolo Annicchiarico, Yanling Wei, Edward Charles Brummer

Bibliographic record

VenuePlant Breeding · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersSamuel Roberts Noble Foundation
KeywordsGermplasmBiologyGenetic diversityPopulationHeterosisGenetic variationGenetic variabilityHybridEvolutionary biologyGeneticsGenotypeBotanyDemographyGene

Abstract

fetched live from OpenAlex

Abstract Semi‐hybrids between genetically distant alfalfa ( Medicago sativa subsp. sativa ) populations may display heterosis whose extent is affected by the structure of genetic diversity across populations. This study aimed to assess the genetic diversity across three putative heterotic populations, one Italian, one Egyptian and one of semi‐erect germplasm from Eastern Europe, Canada and Spanish Mielga (EECM population). Each population was bred from ten parents after various selection cycles. Fifteen genotypes per population were characterized by 20 polymorphic SSR markers. The among‐population variance was over eightfold smaller than the average within‐population variance (2.05 vs. 17.24) and accounted for 10.6% of the total variation. G ’ ST = .090 across markers indicated modest population differentiation. Various diversity measures, multidimensional scaling, and cluster analysis of the genetic structure indicated that the Italian population was more distant from the EECM population than the Egyptian one. The EECM and Egyptian populations were the most distant geographically and genetically. EECM displayed widest intrapopulation variation, accordingly to its constitutive geographical diversity. In conclusion, this study indicates modest genetic differentiation between alfalfa populations even for geographically distant germplasm.

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 score0.840
Threshold uncertainty score0.118

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 head0.062
GPT teacher head0.254
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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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