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Record W2088701641 · doi:10.1139/g07-096

Diversity in coffee assessed with SSR markers: structure of the genus <i>Coffea</i> and perspectives for breeding

2008· article· en· W2088701641 on OpenAlexvenueno aff
Philippe Cubry, Pascal Musoli, Hyacinte Legnaté, David Pot, Fabien De Bellis, Valérie Poncet, François Anthony, Magali Dufour, Thierry Leroy

Bibliographic record

VenueGenome · 2008
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCoffeaBiologyCoffea canephoraMicrosatelliteTransferabilityGenetic diversityCoffea arabicaGenusMolecular breedingBotanyEvolutionary biologyGeneticsGenePopulation

Abstract

fetched live from OpenAlex

The present study shows transferability of microsatellite markers developed in the two cultivated coffee species (Coffea arabica L. and C. canephora Pierre ex Froehn.) to 15 species representing the previously identified main groups of the genus Coffea. Evaluation of the genetic diversity and available resources within Coffea and development of molecular markers transferable across species are important steps for breeding of the two cultivated species. We worked on 15 species with 60 microsatellite markers developed using different strategies (SSR-enriched libraries, BAC libraries, gene sequences). We focused our analysis on 4 species used for commercial or breeding purposes. Our results establish the high transferability of microsatellite markers within Coffea. We show the large amount of diversity available within wild species for breeding applications. Finally we discuss the consequences for future comparative mapping studies and breeding of the two cultivated species.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.046
GPT teacher head0.267
Teacher spread0.222 · 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

Citations74
Published2008
Admission routes1
Has abstractyes

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