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Record W2765380555 · doi:10.5539/jas.v9n11p267

Intercropping of Cowpea with Eucalyptus in Northern Brazil

2017· article· en· W2765380555 on OpenAlexvenueno aff
Manoel Mota dos Santos, Gilberto Coutinho Machado Filho, Rogel Galvão Prates, Raimundo Wagner de Souza Aguiar, Tânia Rodrigues Peixoto Sakai, Weslany Silva Rocha, Rodrigo Ribeiro Fidélis

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsIntercroppingSowingAgronomyCultivarMathematicsPoint of deliveryCropBiologyHorticulture

Abstract

fetched live from OpenAlex

Intercropping consist in growing two or more species with different vegetative cycles and architectures, simultaneously cultivated in same field and same period of time, not necessarily having been sown at the same time. The objective of this study was to evaluate different populations of cowpea development intercropped with Eucalyptus, in order to determine the number of rows of cowpea generates higher grain productivity. This work was carried out at Universidade Federal do Tocantins, in Gurupi, Tocantins, Brazil. The treatments were arranged for different densities of cowpea rows: eight rows, six rows, and four rows of cultivars BRS Nova Era and BRS Sempre Verde, also a control in conventional cropping. The cowpea sowing occurred after 12 months eucalyptus planting. The evaluated characteristics were Flowering (FLOW), Mass of 100 grain (GM), Number of grains per green pod (NGP), Chlorophyll Index (CI) and Grain yield (GY). The density of eight rows have obtained better results for most characteristics, being the density limiting factor for bean production could unfeasible the intercropping system. The BRS Nova Era responds better on most characteristics, probably for a better adaptation to the environment and hold superior characters than BRS Sempre Verde.

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 score0.735
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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