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Record W2588653137 · doi:10.5539/jas.v9n3p245

Heterosis and Specific Combining Ability in Sweet Corn and Its Correlation with Genetic Similarity of Inbred Lines

2017· article· en· W2588653137 on OpenAlexvenueno aff
Purwito Djoko Yuwono, Rudi Hari Murti, Panjisakti Basunanda

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsHeterosisInbred strainDiallel crossHybridGenetic similarityBiologySimilarity (geometry)Mating designRandomized block designGenetic distanceBiotechnologyGeneticsGenetic variationGenetic diversityHorticulturePopulationArtificial intelligenceGeneComputer science

Abstract

fetched live from OpenAlex

The heterosis phenomena has been exploited in hybrid maize field production. Theoretically, heterosis was depending on genetic distance of inbred lines. Meanwhile, different from field corn breeding, sweet corn does not have well defined heterotic group. The objective of this study was to determine genetic similarity (GS) of eight selected inbred lines of sweet corn based on morphological traits and its correlation with specific combining ability (SCA) and heterosis. The eight inbred lines were characterized and crossed in full diallel design. Then, the lines, F1 and its reciprocal, were evaluated by using the Randomized Complete Block Design in three replications. The result showed positive heterosis and SCA effect in most of the hybrids which indicated the heterosis effect in the hybrids performance. Based on the morphological traits, the genetic similarity between the inbred lines was 62.2-82.4%. The inbred lines with 70% similarity coefficient were grouped into three. There was significant correlation between the genetic similarity and heterosis in all traits, except in the plant height. Meanwhile, only SCA of days to maturity and TSS showed significant correlation to genetic similarity. This indicated the usefullness of genetic similarity to identify the potential inbred lines for parental hybrid.

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)0.000
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 score0.584
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.039
GPT teacher head0.222
Teacher spread0.183 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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