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Record W2741312826 · doi:10.5376/rgg.2017.08.0001

Association Analysis of Drought and Yield Related Traits in Upland Land Races of Rice

2017· article· en· W2741312826 on OpenAlexvenueno aff
Swapan K. Tripathy, Sasmita Dash, A. M. Prusti, Reshmi RajKR, Mihir Ranjan Mohanty, Somnath Panda, Asit Prasad Dash, Pavitra Mohan Mohapatra, Kartik Chandra Pradhan

Bibliographic record

VenueRice Genomics and Genetics · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsUpland riceYield (engineering)AgronomyAssociation (psychology)GeographyBiologyOryza sativaGeneticsGenePsychology

Abstract

fetched live from OpenAlex

A set of ninety six land races including a few popular upland rice varieties were assessed to study inter-relationship of drought and yield related traits.  Number of ear bearing tillers/m2, panicle weight and fertility percentage correlated significantly with grain yield/ha and the latter two component traits have very high inter se significant positive correlation under drought stress. Bold kernel type was shown to have significant positive association with grain fertility percentage. Plant height and seed yield under drought stress was negatively associated with leaf rolling score which in turn had inverse relationship with flowering and maturity duration. Thus, drought tolerance in upland rice varieties could be assessed in terms of improved grain filling and such characteristic feature may be associated with genotypes having intermediate plant height, moderate flowering and maturity duration.

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: Observational
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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