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Record W2801657916 · doi:10.1002/ece3.4053

Development of high transferability cp<scp>SSR</scp> markers for individual identification and genetic investigation in Cupressaceae species

2018· article· en· W2801657916 on OpenAlexaff
Lisha Huang, Yan‐Qiang Sun, Yuqing Jin, Qiong Gao, Xian‐Ge Hu, Fu‐Ling Gao, Xiaolei Yang, Ji‐Jun Zhu, Yousry A. El‐Kassaby, Jian‐Feng Mao

Bibliographic record

VenueEcology and Evolution · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central Universities
KeywordsCupressaceaeTransferabilityIdentification (biology)BiologyGenetic markerGeneticsComputational biologyBotanyGeneComputer science

Abstract

fetched live from OpenAlex

Abstract Given the low substitution rate in plastomes, the polymorphic and codominant nature of chloroplast SSR s (cp SSR s) makes them ideal markers, complementing their nuclear counterpart. In Cupressaceae, cp SSR s are mostly paternally inherited, thus, they are useful in mating systems and pollen flow studies. Using e‐ PCR , 92 SSR loci were identified across six Cupressaceae plastomes, and primers were designed for 26 loci with potential interspecific transferability. The 26 developed cp SSR s were polymorphic in four genera, Platycladus , Sabina , Juniperus, and Cupressus and are suitable for Cupressaceae molecular genetic studies and utilization. We genotyped 192 Platycladus orientalis samples from a core breeding population using 10 of the developed cp SSR s and 10 nuclear SSR s, and these individuals were identified with high confidence. The developed cp SSR s can be used in (1) a marker‐assisted breeding scheme, specifically when paternity identification is required, (2) population genetics investigations, and (3) biogeography of Cupressaceae and unraveling the genetic relationships between related 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.300

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 head0.014
GPT teacher head0.218
Teacher spread0.204 · 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

Citations33
Published2018
Admission routes1
Has abstractyes

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