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Record W2110858840 · doi:10.5539/jas.v3n2p230

Combining Ability Studies for Development of New Hybrids over Environments in Sunflower (Helianthus annuus L.)

2011· article· en· W2110858840 on OpenAlexvenueno aff
B. Satish Chandra, Sushil Kumar, A.R.G. Ranganadha

Bibliographic record

VenueJournal of Agricultural Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSunflower and Safflower Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsHybridSunflowerHelianthus annuusBiologyInbred strainHorticultureYield (engineering)HeterosisBiotechnologyVeterinary medicineGeneGeneticsMedicine

Abstract

fetched live from OpenAlex

Seven CMS lines were crossed with six inbred lines in Line x Tester fashion to elucidate the information on the nature of gene action involved in the inheritance of important quantitative traits and to select the parents with good gca and crosses with good sca effects. The resultant 42 hybrids were evaluated along with their parents with three standard checks at three locations in Andhra Pradesh state viz., Hyderabad, Tandur, and Jagtial. The pooled analysis of variance for combining ability revealed that sca variance was higher in magnitude compared to gca variance for all the characters except oil content indicating the preponderance of non-additive gene action for all the characters while additive gene action for oil content. The gca effects of the parents in pooled analysis revealed that among the lines ARM 243B and CMS 17B and among the testers RHA-6D-1R, RES-834-1 and 3376R were found to be promising general combiners for seed yield and yield component characters. Based on significant sca effects in pooled analysis, five hybrids viz., CMS 89A x RES-834-1, CMS 17A x LTRR 341, ARM 243A x R 298, ARM 238A x 3376R and CMS 852A x R-649 were identified as promising for seed yield and other yield contributing characters.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.187

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 communication0.0000.001
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 head0.074
GPT teacher head0.278
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 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

Citations17
Published2011
Admission routes1
Has abstractyes

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