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Record W3153812813 · doi:10.1007/s13595-021-01051-6

A genomic dataset of single‐nucleotide polymorphisms generated by ddRAD tag sequencing in Q. petraea (Matt.) Liebl. populations from Central-Eastern Europe and Balkan Peninsula

2021· article· en· W3153812813 on OpenAlexfundno aff
Endre Gy. Tóth, Zoltán A. Köbölkuti, Klára Cseke, József D. Kámpel, Roland Takács, Vladimir T. Tomov, Péter Ábrán, Srđan Stojnić, Erna Vaštag, Mилaн Maтaругa, Vanja Daničić, Egzon Tahirukaj, P. Zhelev, Saša Orlović, Attila Benke, Attila Borovics

Bibliographic record

VenueAnnals of Forest Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaBulgarian National Science FundManitoba Agriculture, Food and Rural Development
KeywordsBiologySingle-nucleotide polymorphismMetadataPopulationSNPEvolutionary biologyBalkan peninsulaGeneticsQuercus petraeaGeographyGenotypeBotanyGeneDemographyComputer scienceEcologyWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Key message This genomic dataset provides highly variable SNP markers from georeferenced natural Quercus petraea (Matt.) Liebl. populations collected in Bulgaria, Hungary, Romania, Serbia, Bosnia and Herzegovina, Kosovo* and Albania. These SNP loci can be used to assess genetic diversity, differentiation, and population structure, and can also be used to detect signatures of selection and local adaptation. The dataset can be accessed at https://doi.org/10.5281/zenodo.3908963/ (Tóth et al. 2020 ). Associated metadata available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/b6fee4fa-01e9-44d0-92f5-ad19379f9693 .

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.009
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.008

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 head0.048
GPT teacher head0.274
Teacher spread0.226 · 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 designObservational
Domainnot available
GenreDataset

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

Citations9
Published2021
Admission routes1
Has abstractyes

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