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Optimal experimental plot size for papaya cultivation

2019· article· en· W2980940249 on OpenAlexaff
Mauricio dos Santos da Silva, Sebastião de Oliveira e Silva, Sérgio Luíz Rodrigues Donato, Carlos Alberto da Silva Lédo, Orlando Melo Sampaio Filho, Gilmara de Melo Araújo Silva, Antônio Leandro da Silva Conceição

Bibliographic record

VenuePesquisa Agropecuária Brasileira · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsDiscovery Air (Canada)
FundersEmbrapa Mandioca e FruticulturaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsMathematicsPlateau (mathematics)Yield (engineering)AgronomyHorticultureBiology

Abstract

fetched live from OpenAlex

Abstract: The objective of this work was to determine the optimal size of experimental plots for the evaluation of agronomic characteristics and fruit quality of papaya, by the linear model of plateau response, under soil and climatic conditions of the Recôncavo Baiano region, in the state of Bahia, Brazil. The experiment consisted of a uniformity test, with the papaya lineage L78, at 3×2 m spacing, in 16 rows with 22 plants, totaling 352 plants and 2,112 m2 useful area. Each plant was considered as a basic unit, and 11 forms of pre-established plots, with rectangular and row formats, were obtained. The agronomic characteristics and fruit quality were evaluated in the plots. Optimal plot size varied greatly among the variables related to agronomic characteristics, with a greater participation of the variable number of marketable fruit per plant at 14 months (16 basic units). The optimal plot size for the evaluation of the agronomic characteristics and fruit quality in papaya is eight experimental units, with 48 m2 area, at a spacing of 3 m between rows and 2 m between papaya plants.

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 categoriesInsufficient payload (model declined to judge)
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 score0.834
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.275
Teacher spread0.244 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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