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Record W2370407192

Effect of Ecologic Environment on Economic Traits and the Differentiation of Subspecies Traits in F_2 Generation of Cross between Indica and Japonica

2012· article· en· W2370407192 on OpenAlexvenueno aff
Zhengjin Xu

Bibliographic record

VenueSeed · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsJaponicaSubspeciesPanicleBiologyBotanyEcology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this research was to reveal the rules of subspecies characteristics between Japonica and Indica,economic traits and the relationship between the both by using the F2 generation of crossing between Qishanzhan(Indica) and Akihikari(Japonica),which planted in Guangdong Province and Liaoning Province separately.The results showed that the subspecies characteristics of the F2 group,planted in Liaoning and Guangdong province,separately appeared the normal distribution,which indicated the function of genetic recombination played a leading role.Simultaneously,there was prominent affection on the differentiation of Japonica and Indica by environment in reproductive growing stage of rice.And showed that there was a significant differentiation in economic traits,and the appearance in Liaoning was better than which in Guangdong on economic traits for panicle number,kilograin weight and characteristics of primary panicle branches.But things were different on yield,spikelet number and seed setting rate which on Japonica type and Japonica-clinous type were higher in Liaoning and on Indica type and Indica-clinous type were higher in Guangdong,which probably resulted from Ecological adaptation.Simultaneously,the aboved results had also been validated by Relationship Analysis.Furthermore,the article also elaborates some relational questions about made a further research on the differentiation between Japonica and Indica.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.218

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 studies0.0000.001
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 head0.010
GPT teacher head0.211
Teacher spread0.200 · 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
Published2012
Admission routes1
Has abstractyes

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