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Record W2971259423 · doi:10.5539/jas.v11n15p1

Grain Quality and Yield of Rice in the Main and Ratoon Harvests in the Southern U.S.

2019· article· en· W2971259423 on OpenAlexvenueno aff
Haiya Cai, Rodante E. Tabien, Deze Xu, Chersty L. Harper, Jason Samford, Yuanyuan Yang, Aiqing You, Stanley Omar PB. Samonte, Leon Carl Holgate, Chunhai Jiao

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsYield (engineering)CropGrain yieldMathematicsField experimentGrain qualityHorticultureAgronomyBrown riceWhite riceAnimal scienceBiologyFood scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The ratoon rice system is an energy-saving, high-efficiency cultivation method. Harvests from a two-year field trial with a main crop (MC) and a ratoon crop (RC) were used to evaluate milled grain quality traits and yield performance. The results indicated that chalkiness was significantly lower in the RC than in the MC. Chalkiness ranged from 1.90 to 15.01%, with an average of 6.46%, in the MC and from 0.66 to 3.28%, with an average of 1.50%, in the RC across two years. In addition, nearly all of the RC of the test entries had lower white vitreous (higher translucency) than the MC of the same entry. In 6 of the 20 entries, the MC had longer or wider milled grain than the RC in 2017. The milled rice recovery for the MC was higher in both years, but there was no difference in head rice recovery within the same year. The average total yield (MC+RC) in the two years was 12.6 and 13.0 t/ha, and the two-year average RC yields were 47.5 and 37.3% those of the MC. Our results revealed that the RC milled grains showed better appearance quality than the MC grains, and several genotypes had comparable or even better milled grain quality and yield compared with the check entries that were suitable for the ratoon rice system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.097

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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