MétaCan
Menu
Back to cohort
Record W2807253316 · doi:10.2298/gensr1801069n

Relationships among some pines from subgenera Pinus and Strobus revealed by nuclear EST-microsatellites

2018· article· en· W2807253316 on OpenAlexaff
Biljana Nikolić, Dragan Kovačević, Snežana Mladenović-Drinić, Ana Nikolić, Zorica S. Mitić, Srdjan Bojović, Petar D. Marin

Bibliographic record

VenueGenetika · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsInstitute for Biological Sciences
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsSubspeciesBiologyPinus <genus>MicrosatelliteSubgenusPinus pinasterBotanyTaxonGenusAlleleZoologyGeneticsGene

Abstract

fetched live from OpenAlex

Genetic relationships among 12 taxa from subgenera Pinus and Strobus were studied through fourteen microsatellite markers, previously developed for Pinus taeda. To our knowledge, this is the first comparative study of pines using nuclear EST-microsatellites (EST-SSRs). The total number of detected alleles in all investigated taxa was 72 (5.14 in average). The numbers of alleles per locus and PIC values for estimated markers ranged from 3 to 7, and from 0.43 to 0.81, respectively. Presented results are in accordance with majority of previous genetic investigations and infrageneric classification of genus Pinus up to the sectional level, while subsectional position of some species has still not dismissed, especially regarding relict ones. According to nuclear EST-SSRs, Pinus heldreichii is in early-diverging position within subsection Pinaster and shows the greatest closeness with P. halepensis, while Pinus peuce doesn't have basal position within subsection Strobus being more close to P. strobus than to P. wallichiana. Furthermore, the closest connections in subsection Pinus were found between two Pinus nigra subspecies (dalmatica and nigra) as well as between P. sylvestris and P. mugo.

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 score0.695
Threshold uncertainty score0.651

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueGenetikaSame topicYeasts and Rust Fungi StudiesFrench-language works237,207