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Record W3211901611 · doi:10.5539/jas.v13n12p71

Broccoli Production With Regulated Deficit Irrigation at Different Phenological Stages

2021· article· en· W3211901611 on OpenAlexvenueno aff
Daniele de Souza Terassi, Roberto Rezende, Gustavo Soares Wenneck, Cláudia Salim Lozano Menezes, André Felipe Barion Alves Andrean, Vinícius Villa e Vila, Lucas Henrique Maldonado da Silva

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsInflorescenceDry matterPhenologyAgronomyCropCompletely randomized designIrrigationBiologyDeficit irrigationProductivityHorticultureIrrigation management

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate the effects of different levels of soil water replacement in each phenological stage of broccoli crop cultivated in a protected environment. The experiment was conducted in a protected environment in the municipality of Maringá, Paraná, Brazil. The experimental design used was in randomized blocks 4 by 3 having four levels of water replacement (55, 70, 85, and 100% of crop evapotranspiration) applied in three phenological stages (initial, intermediate, and final), and four replications. Productivity, inflorescence fresh matter, leaf fresh matter, stem fresh matter, number of leaves, stem diameter, inflorescence height, inflorescence diameter, plant height, leaf area, inflorescence dry matter, stem dry matter and leaf dry matter were evaluated. The data were submitted to variance analysis and compared by Scott-Knott test and regression analysis. Deficit of 30% of the ETc during the final stage of the broccoli crop, reduced productivity by 7%, on the other hand for the initial and intermediate stages, there was a drop of 30% and 23% respectively. The water deficit caused significant losses in broccoli production during the first phenological stages, but the final stage was less critical.

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 score0.837
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.205
Teacher spread0.188 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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