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Record W2914091277 · doi:10.15287/afr.2018.1191

Characterization of 20 new EST-SSR markers for northern red oak (Quercus rubra L.) and their transferability to Fagus sylvatica L. and six oak species of section Lobatae and Quercus

2018· article· en· W2914091277 on OpenAlexaboutno aff
Oliver Gailing

Bibliographic record

VenueAnnals of Forest Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
FundersBundesministerium für Ernährung und LandwirtschaftHuron Mountain Wildlife FoundationU.S. Department of Agriculture
KeywordsQuercus petraeaFagaceaeQuercus roburFagus sylvaticaBiologyBeechBotany

Abstract

fetched live from OpenAlex

Northern red oak (Quercus rubra L.) is widely distributed in the eastern United States and southeastern Canada. It has also been introduced to Europe, where it has become an economically important plantation species now. Despite growing genomic resources, the number of available EST-SSR (expressed sequence tag – simple sequence repeat) markers for Q. rubra is still limited. Here, we used existing sequence information to provide a new set of EST-SSRs for northern red oak. In total, we report 20 polymorphic EST-SSRs, for which performance was evaluated in three Q. rubra populations from different regions in Michigan. We further tested the transferability of these markers to six additional oak species of section Lobatae (Quercus ellipsoidalis E.J. Hill, and Quercus georgiana M.A. Curtis) and Quercus (Quercus robur L., Quercus alba L., Quercus pedunculiflora K. Koch, and Quercus petraea (Matt.) Liebl.), as well as to European beech (Fagus sylvatica L.). The reported markers can be used in future population genetic studies.

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)0.001
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 score0.501
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.054
GPT teacher head0.329
Teacher spread0.274 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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