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Multiplexed microsatellite markers for genetic studies of beech

2011· article· en· W1537455928 on OpenAlexfundno aff
Sophie D. Lefevre, Stefanie Wagner, Rémy J. Petit, Guillaume de Lafontaine

Bibliographic record

VenueMolecular Ecology Resources · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsBeechBiologyMicrosatelliteMultiplexGenotypingLinkage disequilibriumFagus sylvaticaGeneticsAlleleMultiplex polymerase chain reactionEvolutionary biologyComputational biologyGenotypeHaplotypeEcologyPolymerase chain reactionGene

Abstract

fetched live from OpenAlex

European beech (Fagus sylvatica L.) is one of the economically most important broadleaved tree species in Europe and has become a model for studying climate change effects on forests. Multiplex PCR of microsatellites is a fast and cost-effective technique allowing high-throughput genotyping. Here we present the procedure used to develop two multiplex kits (8-plexes) for European beech. We paid particular attention to quality control throughout all steps of the multiplex kits development (null allele detection, error rate measurements, linkage disequilibrium). Preliminary assays suggest that the 16 amplified loci are largely devoid of null alleles and allow rapid and cost-effective genotyping of beech with low error rates. The two kits, which differ in their levels of polymorphism, most likely due to marker origin, were also informative in seven other beech species tested.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2011
Admission routes1
Has abstractyes

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