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Record W2982247587 · doi:10.3389/fpls.2019.01341

Two Loci, RiAF3 and RiAF4, Contribute to the Annual-Fruiting Trait in Rubus

2019· article· en· W2982247587 on OpenAlexaboutno aff
Rubina Jibran, Jessica A. Spencer, Gina E. Fernandez, Amparo Monfort, Mourad Mnejja, Helge Dzierzon, Jibran Tahir, Kevin M. Davies, David Chagné, Toshi Foster

Bibliographic record

VenueFrontiers in Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
FundersSerono Symposia International Foundation
KeywordsRubusBiologyBlowing a raspberryPopulationPetalBotanyHorticulture

Abstract

fetched live from OpenAlex

Most Rubus species have a biennial cycle of flowering and fruiting with an intervening period of winter dormancy, in common with many perennial fruit crops. Annual-Fruiting varieties of raspberry (R. idaeus and R. occidentalis L.) and blackberry (R. subgenus Rubus) are able to flower and fruit in one growing season, without the intervening dormant period normally required in Biennial-Fruiting (BF) varieties. We used a red raspberry (R. idaeus) population segregating for AF obtained from a cross between NC493 and ‘Chilliwack’ to identify genetic factors controlling AF. Genotyping by sequencing was used to generate saturated linkage maps in both parents. Trait mapping in this population indicated that AF is controlled by two newly identified loci (RiAF3 and RiAF4) located on Rubus linkage groups (LGs) 3 and 4. The location of these loci was analyzed using single nucleotide polymorphism (SNPs) markers on independent red raspberry and blackberry populations segregating for the AF trait. This confirmed that AF in Rubus is regulated by loci on LG 3 and 4, in addition to a previously reported locus on LG 7. Comparative RNAseq analysis at the time of floral bud differentiation in an AF and a BF variety revealed candidate genes potentially regulating the trait.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score0.124

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.238
Teacher spread0.225 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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