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Record W4220926454 · doi:10.5376/mgg.2022.13.0002

Genetic Diversity Analysis of Maize in Baoshan Yunnan by SSR Markers

2022· article· en· W4220926454 on OpenAlexvenueno aff
Li Song, Delin Shi, Dengji Lou, Shi Yundong

Bibliographic record

VenueMaize Genomics and Genetics · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic diversityGenetic similarityAlleleSimilarity (geometry)BiologyGenetic distanceDiversity indexVeterinary medicineStatisticsBiotechnologyGenetic variationMathematicsGeneticsDemographyPopulationEcologySociologyGeneComputer science

Abstract

fetched live from OpenAlex

The genetic diversity of 15 maize and it’s popularization grown in Baoshan city were carried out based on SSR primers recommended by Chinese agricultural industry standard (NY/T1432-2014). The results showed that 17 pairs of SSR primers presented polymorphic, and 72 alleles were detected. The number of alleles detected by each pair of primers was 3 to 6, with an average of 4.3. The effective number of alleles was 1.219 to 1.750, with an average of 1.448. The Nei’s genetic diversity index was 0.176 to 0.418, with an average of 0.283. The Shannon information index was 0.315 to 0.605, with an average of 0.444. The SSR polymorphic information (PIC) distribution ranges from 0.512 to 0.812, with an average of 0.686. The cluster analysis showed that the genetic similarity coefficient ranged from 0.417 to 0.819, with an average of 0.632, when the genetic similarity coefficient was 0.65, 15 varieties were divided into 5 groups. The results can provide theoretical and technical support for breeding and promotion of Baoshan maize varieties in Yunnan province.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.799

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 science0.0000.001
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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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