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Record W2896928715 · doi:10.5539/jas.v10n11p138

Sowing Time of Sweet Corn in Summer Season in Northwestern Paraná, Brazil

2018· article· en· W2896928715 on OpenAlexvenueno aff
Antônio Augusto Nogueira Franco, Pedro Soares Vidigal Filho, Carlos Alberto Scapim, Ricardo Shigueru Okumura, Alberto Yuji Numoto, Odair José Marques

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
FundersFundação AraucáriaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSowingRandomized block designHybridPhenologyYield (engineering)Leaf area indexGrowing seasonMathematicsAgronomyHorticultureFactorial experimentBiologyStatistics

Abstract

fetched live from OpenAlex

The aims of study were to evaluate the effect of sowing time in summer season on agronomic characteristics of sweet corn grown in summer season in Northwest Paraná, Brazil. The experiments were conducted in 2011, and 2012, in both years, the sowing was carried out on ST1: 09/05; ST2: 09/19; ST3: 10/03; ST4: 10/17, and ST5: 10/31. The experimental design was a randomized block design in a 5 × 2 factorial scheme, consisting of sowing time and two corn hybrids, with four replicates. The evaluated variables were plant height, leaf area index and yield of commercial ears, in which the effects of sowing times were studied through regression analysis, and effects of hybrids and agricultural years were studied by F-test. Sowing times promoted reductions in phenology of hybrids, but did not provide variations in degree-days accumulation by hybrids. The maximum leaf area index (3.82) was estimated for 10/04. In year 2011, the maximum plant height (2.17 m) and yield (13.0 Mg ha-1) were estimated for 10/06, and 10/03, respectively. While, in year 2012, the maximum height (2.22 m) and yield (12.85 Mg ha-1) were obtained on 10/09 and 10/11, respectively. The hybrid RB6324 showed greater yield potential, independent of year.

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 score0.607
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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
Published2018
Admission routes1
Has abstractyes

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