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Record W4389085849 · doi:10.53555/sfs.v9i1.1806

Unraveling The Genetics Of Rice (Oryza Sativa L.) Yield And Its Component Traits

2022· article· en· W4389085849 on OpenAlexvenueno aff
Ravi Kishan Soni

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsPanicleOryza sativaRandomized block designPath coefficientBiologyPath analysis (statistics)Grain yieldTest weightAgronomyHorticultureMathematicsStatisticsGeneticsGene

Abstract

fetched live from OpenAlex

In the Hadoti region of Rajasthan, rice (Oryza sativa L.) crosses were grown in a Randomized Block Design for the purpose of estimating correlation and path coefficient in an experimental study. An examination of variance data demonstrated that there were noteworthy distinctions among all genotypes with respect to the traits that were studied. The correlation or association studies disclosed that the relation of grain yield per plant was positively and significantly at phenotypic and genetic level with characters viz. number of effective tillers per plant, panicle length, number of spikelets per panicle, test weight and kernel length. The highest positive direct impression/ effect on grain yield was recorded for number of spikelets per panicle, panicle length, kernel length and number of effective tillers per plant.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.248
GPT teacher head0.242
Teacher spread0.006 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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