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Record W2907145198 · doi:10.1002/aps3.1208

Characterization of 30 microsatellite markers in distylous <i>Primula sinolisteri</i> (Primulaceae) using HiSeq sequencing

2019· article· en· W2907145198 on OpenAlexaff
Xinjia Wang, Li Zhong, Zhikun Wu, Huaying Sun, Hong Wang, Li D, Spencer C. H. Barrett, Wei Zhou

Bibliographic record

VenueApplications in Plant Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of Toronto
FundersInstitute of Botany, Chinese Academy of SciencesKunming Institute of Botany, Chinese Academy of SciencesNational Natural Science Foundation of China-Yunnan Joint FundChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPrimulaceaeBiologyMicrosatellitePrimulaBotanyHeterostylyGeneticsEvolutionary biologyGeneAllelePollination

Abstract

fetched live from OpenAlex

Premise of the Study Microsatellite markers were developed for Primula sinolisteri , a perennial distylous herb belonging to section Obconicolisteri (Primulaceae), to facilitate future investigations of the population genetics and mating patterns of populations in this species. Methods and Results We developed 30 microsatellite markers for P. sinolisteri using HiSeq X‐Ten sequencing and measured polymorphism and genetic diversity in a sample of 36 individuals from three natural populations. The markers displayed relatively high polymorphism, with the number of observed alleles per locus ranging from one to 19 (mean = 4.42). The observed and expected heterozygosity ranged from 0–1.000 and 0.083–0.882, respectively. Twenty‐nine of the loci were also successfully amplified in homostylous P. sinolisteri var. aspera . Conclusions The microsatellite markers we have identified in P. sinolisteri provide powerful tools for investigating patterns of population genetic diversity and the evolutionary relationships between heterostyly and homostyly in this species.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.303

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 head0.022
GPT teacher head0.250
Teacher spread0.228 · 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 designBench or experimental
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

Citations4
Published2019
Admission routes1
Has abstractyes

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