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Record W2750693234 · doi:10.5539/jas.v10n3p290

Rice Yield Components under Water Stress Imposed at Different Growth Stages

2018· article· en· W2750693234 on OpenAlexvenueno aff
Germani Concenço, José Maria Barbat Parfitt, Ivana Santos Moisinho, Marcos Valle Bueno, Jaqueline Trombetta da Silva, Samara Emerim Concenço

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsPanicleRandomized block designAgronomyGreenhouseSterilityWater stressBiologyHorticultureMathematicsBotany

Abstract

fetched live from OpenAlex

We aimed to assess rice yield components as function of water stress imposed at distinct crop growth stages under greenhouse, in randomized block design under factorial scheme 3 × 4 + 1, with four replications. The factor “A” was the growth stage in which water stress was imposed, being (a) vegetative, (b) reproductive 1, and (c) reproductive 2; factor “B” was four levels of water stress (0-200 kPa). There was also an additional treatment consisting of a flooded check. Water was replenished to saturation every time the threshold stress was reached. At the end of the cycle, all panicles were counted and collected, per plant, for further analysis. In the lab, grains per panicle were counted, being classified either as whole kernel or aborted grains, whose results were used for obtaining sterility percentage. Whole kernel grains were weighted for obtaining the 1000 grains weight for each treatment, and the consequent plant grain yields. Number of panicles was not affected when stress was imposed after panicle initiation, but when imposed at tillering it was reduced; at grain filling, water stress promoted grains sterility higher than 90%; grain weight was only reduced when carbohydrates were directed to root formation in detriment of grain filling; lower rice grain yield per plant was observed even when treatments were maintained above 10kPa all along the cycle, compared to the flooded treatment.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.650

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.039
GPT teacher head0.234
Teacher spread0.195 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

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