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Record W2236803698 · doi:10.5376/lgg.2016.07.0002

Estimation of Genetic Variability and Character Association in Micro Mutant Lines of Greengram [<i>Vigna Radiata</i> (L.) Wilczek] for Yield Attributes and Cold Tolerance

2016· article· en· W2236803698 on OpenAlexvenueno aff
P. Panigrahi

Bibliographic record

VenueLegume Genomics and Genetics · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoint of deliveryVignaHeritabilityRadiataBiologyHorticultureYield (engineering)Path analysis (statistics)Genetic correlationGenetic variationBotanyGeneMathematicsGeneticsStatistics

Abstract

fetched live from OpenAlex

Thirty genotypes of greengram including 22 mutant lines, two parents and two standard varieties along with four land races were evaluated in R.B.D. for yield and component traits. The PCV and GCV estimates were high for response to cold of 10, 30 and 40 days old seedlings of green gram at 10 oC temperature. Plant height and pods plant -1 had high heritability with high genetic advance indicating additive gene action. Characters like100-seed weight and seed pod -1 were with high-moderate heritability but low genetic advance indicating non-additive gene effect. Plant height, cluster plant -1 , Pods plant -1 , pod length and seeds pod -1 showed significant positive correlation with yield. Path-analysis showed that pods plant -1 had highest direct positive effects on yield followed by plant height. Positive correlation of most traits with yield was greatly influenced by indirect positive effect via pods plant -1 and plant height.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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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