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Record W1968738531 · doi:10.1139/g02-070

Heterologous nuclear and chloroplast microsatellite amplification and variation in tea,<i>Camellia sinensis</i>

2002· article· en· W1968738531 on OpenAlexvenueno aff
Shiv Shankhar Kaundun, Satoru Matsumoto

Bibliographic record

VenueGenome · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
Fundersnot available
KeywordsMicrosatelliteBiologyCamellia sinensisGeneticsGenetic diversityNuclear DNALocus (genetics)Chloroplast DNAAllelePopulationGenomeBotanyMitochondrial DNAGene

Abstract

fetched live from OpenAlex

The advantage of the cross transferability of heterologous chloroplast and nuclear microsatellite primers was taken to detect polymorphism among 24 tea (Camellia sinensis (L.) O. Kuntze) genotypes, including both the assamica and the sinensis varieties. Primer information was obtained from the closely related Camellia japonica species for four nuclear microsatellites, and from Nicotiana tabaccum for seven universal chloroplast microsatellites. All of the nuclear microsatellite loci tested generated an expected DNA fragment in tea, revealing between three and five alleles per locus. Four out of the seven chloroplast microsatellites primers amplified positively, and of these only one was polymorphic with three alleles, which is in agreement with the conserved nature of chloroplast microsatellites at the intraspecific level. A factorial correspondence analysis carried out on all genotypes and nuclear microsatellite alleles separated the assamica and sinensis genotypes into two groups, thus demonstrating the value of these markers in establishing the genetic relationship between tea varieties. Genetic diversity measured with nuclear microsatellites was higher than that measured with other types of molecular markers, offering prospects for their use in fingerprinting, mapping, and population genetic studies, whereas polymorphisms detected at a cpSSR locus will allow the determination of plastid inheritance in the 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.330

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 head0.009
GPT teacher head0.184
Teacher spread0.175 · 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

Citations68
Published2002
Admission routes1
Has abstractyes

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