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Record W2082527761 · doi:10.1139/g06-126

Microsatellite markers are powerful tools for discriminating among olive cultivars and assigning them to geographically defined populations

2006· article· en· W2082527761 on OpenAlexvenueno aff
V. Sarri, Luciana Baldoni, Andrea Porceddu, Nicolò G. M. Cultrera, A. Contento, M. Frediani, Angjelina Belaj, P. G. Cionini

Bibliographic record

VenueGenome · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyCultivarMicrosatelliteGermplasmPopulationSelection (genetic algorithm)Genetic markerMediterranean BasinIdentification (biology)GeneticsBiotechnologyBotanyMediterranean climateGeneAlleleEcology

Abstract

fetched live from OpenAlex

Twelve simple sequence repeat (SSR) loci were used to differentiate among 118 cultivars sampled in several countries of the Mediterranean basin and to analyze the genetic structure of olive cultivar gene pools. The markers were found to have high discrimination power. On average, with a single assay it was possible to discriminate 96% of the pairwise comparisons and, with a combination of 3 loci, virtually all cultivars were distinguished. The SSR markers were also tested for their ability to assign cultivars to their geographic population of origin. A selection of 6 loci was found to maximize assignment accuracy, correctly reallocating up to 75.4% of cultivars to their population of origin. Because of the confusion surrounding the origin of most olive cultivars, their molecular identification and ascertainment of origin will be extremely useful for germplasm management and breeding.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.268
Teacher spread0.215 · 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
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

Citations139
Published2006
Admission routes1
Has abstractyes

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