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Record W2995398547 · doi:10.5539/jps.v9n1p13

Characteristics of Giant Embryo Rice and Research Prospect of Its Processing and Utilization

2019· article· en· W2995398547 on OpenAlexvenueno aff
Bo Peng, Xia-Yu Tian, Kun He, Kun Xu, Juan Peng, Zi-Yue Liu, Xiaorui Ma, Yanfang Sun, Xiaohua Song, Lulu He, Rui-Hua Pang, Jin-Tiao Li, Quanxiu Wang, Wei Zhou, Huilong Li, Hongyu Yuan

Bibliographic record

VenueJournal of Plant Studies · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsnot available
FundersNanhu Scholars Program for Young Scholars of Xinyang Normal UniversityXinyang Normal UniversityNational Natural Science Foundation of China
KeywordsGermplasmEmbryoBiologyBiotechnologyNutrientAgronomyEcology

Abstract

fetched live from OpenAlex

Giant embryo rice is a special kind of functional rice which can produce eutrophic rice. It conforms to people's concept of food consumption and healthy life. Giant embryo rice and its intensively processed products have been widely used in food, medicine, health products and other fields. They have extremely important scientific significance and economic value, and have become one of the most nutritional and health-care functional rice in the future. In recent years, a series of important advances have been made in the research of giant embryo rice. The special nutrients, agronomic characteristics and products developed by giant embryo rice have attracted the attention of rice breeders and consumers at home and abroad. In this paper, the research contents and new advances in the creation and breeding of giant embryo rice germplasm, the characteristics of nutrient changes, important agronomic traits and the processing and utilization of giant embryo rice were summarized, and the application prospect of giant embryo rice was prospected, all of which could provide important reference for the development and sustainable utilization of giant embryo rice.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.066

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.142
GPT teacher head0.363
Teacher spread0.221 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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