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Record W2341448506 · doi:10.1139/gen-2015-0216

Single nucleotide polymorphism analysis reveals heterogeneity within a seedling tree population of a polyembryonic mango cultivar

2016· article· en· W2341448506 on OpenAlexvenueno aff
Patrick Winterhagen, Jens‐Norbert Wünsche

Bibliographic record

VenueGenome · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPostharvest Quality and Shelf Life Management
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsBiologyGeneticsPopulationPolyembryonyEcotypeNucleotide diversitySingle-nucleotide polymorphismGene isoformGenotypeGeneBotanyEmbryoHaplotype

Abstract

fetched live from OpenAlex

Within a polyembryonic mango seedling tree population, the genetic background of individuals should be identical because vigorous plants for cultivation are expected to develop from nucellar embryos representing maternal clones. Due to the fact that the mango cultivar 'Hôi' is assigned to the polyembryonic ecotype, an intra-cultivar variability of ethylene receptor genes was unexpected. Ethylene receptors in plants are conserved, but the number of receptors or receptor isoforms is variable regarding different plant species. However, it is shown here that the ethylene receptor MiETR1 is present in various isoforms within the mango cultivar 'Hôi'. The investigation of single nucleotide polymorphisms revealed that different MiETR1 isoforms can not be discriminated simply by individual single nucleotide exchanges but by the specific arrangement of single nucleotide polymorphisms at certain positions in the exons of MiETR1. Furthermore, an MiETR1 isoform devoid of introns in the genomic sequence was identified. The investigation demonstrates some limitations of high resolution melting and ScreenClust analysis and points out the necessity of sequencing to identify individual isoforms and to determine the variability within the tree population.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.231

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 head0.037
GPT teacher head0.241
Teacher spread0.204 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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