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Record W2890076350 · doi:10.5539/jas.v10n10p206

Agro-economic Feasibility of Intercropped Systems of Radish and Cowpea-Vegetable Manured With Roostertree Biomass

2018· article· en· W2890076350 on OpenAlexvenueno aff
Maria Francisca Soares Pereira, Francisco Bezerra Neto, Aurélio Paes Barros Júnior, Paulo César Ferreira Linhares, Maiele Leandro da Silva, Hamurábi Anízio Lins

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsIntercroppingBiomass (ecology)BiomeRandomized block designAgronomyCropManureEcosystemBiologyAgroforestryEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The association of crops presents as one of the cultivation practices to be used in the systems of vegetable crop production in the northeast Brazil semiarid, fertilized with biomass of spontaneous species of the Caatinga biome as green manure. Under this approach, a study was performed during the period from June to December 2013, in the research area of the Experimental Farm belonging to the Universidade Federal Rural do Semi-árido, Mossoró, RN (Brazil), to assess the feasibility of the agro-economic efficiency of the radish × cowpea-vegetable association manured with different amounts of roostertree biomass in semiarid environment. A randomized complete block design was used with four treatments and five repetitions. The treatments were composed of four biomass amounts of roostertree incorporated to the soil (10, 25, 40 and 55 t ha-1 on a dry basis). The highest agronomic and economic efficiencies of the intercropping of radish with cowpea-vegetable were obtained with the incorporation of 53 and 47 t ha-1 of roostertree biomass added to the soil. The roostertree spontaneous species of the Caatinga biome it is showed as an efficient green manure in the association of radish with cowpea-vegetable.

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 score0.904
Threshold uncertainty score0.304

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 studies0.0000.001
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 head0.024
GPT teacher head0.242
Teacher spread0.218 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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