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Record W3092059804 · doi:10.1094/phyto-12-18-0451-r

Genetic Diversity and Population Structure of<i>Phyllosticta citriasiana</i>in China

2020· article· en· W3092059804 on OpenAlexaff
Yibing Zeng, Tao Xiong, Bei Liu, Elma Carstens, Jianping Xu, Hongye Li

Bibliographic record

VenuePhytopathology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBiologyHeterothallicGenetic diversityGene flowPopulationMating typeGenetic structureSexual reproductionPopulation geneticsGenetic variationEvolutionary biologyGeneticsGeneDemography

Abstract

fetched live from OpenAlex

Phyllosticta citriasiana is the causal agent of citrus tan spot, an important pomelo disease in Asia. At present, there is little or no information on the epidemiology or population structure of P. citriasiana. By using simple sequence repeat markers, we analyzed 94 isolates from three pomelo production regions in southern and southeastern China. The analyses showed high genetic diversity in each of the three geographic populations. A STRUCTURE analysis revealed two genetic clusters among the 94 isolates; one geographic population was dominated by genotypes in one cluster, and the other two geographic populations were dominated by genotypes of the second cluster. P. citriasiana has a heterothallic mating system with two idiomorphs, MAT1-1 and MAT1-2. Analyses using mating type–specific primers revealed that both mating types were present in all three geographic populations, and in all three populations the mating type ratios were in equilibrium. Although the sexual stage of the fungus has not been discovered yet, analyses of allelic associations indicated evidence for sexual and asexual reproduction within and between populations. Despite the observed genetic differentiation between the three geographic populations, evidence for long-distance gene flow was found.

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 score0.450
Threshold uncertainty score0.336

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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