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Record W2153641636 · doi:10.5539/jas.v4n3p227

SRAP Markers and Morphological Traits Could Be Used in Test of Distinctiveness, Uniformity, and Stability (DUS) of Lettuce (Lactuca sativa) Varieties

2011· article· en· W2153641636 on OpenAlexvenueno aff
Lijuan Liu, Zaochang Liu, Hairong Chen, Liguo Zhou, Yunhua Liu, Lijun Luo

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvances in Cucurbitaceae Research
Canadian institutionsnot available
FundersUniversità di Bologna
KeywordsLactucaGermplasmBiologyTraitIdentification (biology)UPGMAGenetic diversityQuantitative trait locusOptimal distinctiveness theoryMolecular markerHorticultureBiotechnologyBotanyGenetic variationGeneticsComputer sciencePopulationMedicine

Abstract

fetched live from OpenAlex

The test of distinctiveness, uniformity, and stability (DUS) is a necessary step for variety identification and new variety application. The objective of this study is to provide molecular marker-assisted approach combined with morphological trait-based testing for more convenient and fast DUS test and identification of varieties. Eighteen pairs of SRAP markers and 40 morphological traits for DUS test were applied for genetic diversity analysis of 50 lettuce (Lactuca sativa) varieties. Average polymorphism information content (PIC) of the SRAP markers was 0.80, ranging from 0.39 to 0.97. Cluster analysis using UPGMA of the band patterns amplified by SRAP marker and morphological trait-based clustering separated the varieties into three groups. The correlation coefficient of SRAP marker and morphological traits was 0.5455 reflecting that the two clustering results shared some similarity and consistence. It revealed that the combination of both SRAP marker and morphological trait analysis is more conducive to proper identification and classification of plant varieties, which will undoubtedly bring an alternative choice to DUS testing of plant new varieties and conservation of plant germplasm.

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)0.001
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 score0.497
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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
Published2011
Admission routes1
Has abstractyes

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