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Record W2271543868 · doi:10.2298/gensr1501205s

Cross-species amplification of nuclear EST-microsatellites developed for other Pinus species in Pinus nigra

2015· article· en· W2271543868 on OpenAlexaff
Zorica S. Mitić, J. Aleksić, Tanja Dodoš, Nemanja Rajčević, Srdjan Bojović, Petar D. Marin

Bibliographic record

VenueGenetika · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsBiologyMicrosatelliteGenetic diversityLocus (genetics)LarchGeneticsAlleleEvolutionary biologyBotanyPopulationGene

Abstract

fetched live from OpenAlex

Due to the current lack of nuclear microsatellites (simple sequence repeats - SSRs) specifically developed for Pinus nigra, an important European coniferous species, we cross-species amplified 12 EST-SSRs (expressed sequence tagged SSRs) developed for other Pinus species in P. nigra in order to delineate loci which can be used for assessing levels of genetic diversity and genetic structuring in this species. We amplified these loci in individuals from seven populations from the central Balkans representing four recognized infraspecific taxa of P. nigra (ssp. nigra, var. gocensis, ssp. pallasiana, and var. banatica). Contrary to expectations on high transferability of EST-SSRs into related species, only three out of 12 tested loci were successfully amplified in P. nigra, but they displayed lack/low levels of polymorphism or generated multilocus amplification products. Thus, our estimates on levels of genetic diversity (HE = 0.183) and genetic differentiation (FST = 0.007) were based on variability of a single locus harboring four alleles only and they should be taken with cautions. Our study highlights the need for the development of high-resolution molecular markers, such as co-dominant genic or genomic SSRs or predominantly biallelic SNPs, or utilization of anonymous dominant markers, such as AFLPs, for genotyping in P. nigra.

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 score0.793
Threshold uncertainty score0.537

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 head0.043
GPT teacher head0.284
Teacher spread0.241 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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