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Record W2159706607 · doi:10.1139/gen-2013-0027

Population genetic structure and genetic diversity of soybean aphid collections from the USA, South Korea, and Japan

2013· article· en· W2159706607 on OpenAlexvenueno aff
Tae‐Hwan Jun, Andrew P. Michel, Jacob A Wenger, Sung‐Taeg Kang, M. A. Rouf Mian

Bibliographic record

VenueGenome · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersAnimal and Plant Health Inspection Service
KeywordsSoybean aphidGenetic diversityBiologyAphidPopulationGenetic structurePEST analysisAphididaeBotanyHomopteraDemography

Abstract

fetched live from OpenAlex

Following its recent invasion of North America, the soybean aphid (Aphis glycines Matsumura) has become the number one insect pest of soybean (Glycine max L. Merr.) in the north central states of the USA. A few studies have been conducted on the population genetic structure and genetic diversity of the soybean aphid and the source of its invasion in North America. Molecular markers, such as simple sequence repeats (SSRs) are very useful in the evaluation of population structure and genetic diversity. We used 18 SSR markers to assess the genetic diversity of soybean aphid collections from the USA, South Korea, and Japan. The aphids were collected from two sites in the USA (Indiana and South Dakota), two sites in South Korea (Yeonggwang district and Cheonan city), and one site in Japan (Utsunomiya). The SSR markers were highly effective in differentiating among aphid collections from different countries. The level of differentiation within each population and among populations from the same country was limited, even in the case of the USA where the two collection sites were more than 1200 km apart.

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 score0.289
Threshold uncertainty score0.984

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)0.0010.000

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 head0.010
GPT teacher head0.170
Teacher spread0.161 · 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
Published2013
Admission routes1
Has abstractyes

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