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Record W2937701449 · doi:10.5539/jas.v11n5p408

Growth of Okra Under Nitrogen Rates and Wastewater in the Brazilian Semiarid Region

2019· article· en· W2937701449 on OpenAlexvenueno aff
Aldair de Souza Medeiros, Sebastião de Oliveira Maia Júnior, Giordano Bruno Medeiros Gonzaga, Thiago Cândido dos Santos, Manoel Moisés Ferreira de Queiroz, Renato Américo de Araújo Neto, Ivomberg Dourado Magalhães, Patrícia da Silva Costa, Jailma Ribeiro de Andrade, Mariana de Oliveira Pereira

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsWastewaterIrrigationNitrogenEnvironmental scienceNitrogen fertilizerRandomized block designWastewater reuseReuseHuman fertilizationSewage treatmentCropAgronomyEnvironmental engineeringBiologyChemistryEcology

Abstract

fetched live from OpenAlex

Water is one of the most important natural resources, especially for semiarid regions where it is very limited. Thus, some alternatives to preserve water are necessary. In this sense, we aimed to evaluate the effect of irrigation with post-treated domestic wastewater associated with different nitrogen rates on the growth of okra in the semiarid region of Brazil. The experiment was performed in the municipality of Pombal, state of Paraíba, Brazil. It was used a randomized block design with six nitrogen rates (N1 = 0, N2 = 40, N3 = 80, N4 = 120, N5 = 160, and N6 = 200 kg ha-1) and wastewater corresponding respectively to 0; 25; 50; 75; 100; and 125% of the fertilization recommendation for the okra crop. In addition to these treatments, a control was added and the plants received 100% of the recommended dose of nitrogen and they were irrigated with water (IW). The control was compared with the treatments that were irrigated with wastewater and received the minimum (0%) and the recommended (100%) doses of nitrogen fertilization. The use of treated wastewater is an excellent technique for the reuse of water in semiarid regions, but it does not fully meet the okra nitrogen requirements.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.129

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 communication0.0000.001
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 head0.007
GPT teacher head0.203
Teacher spread0.197 · 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
Published2019
Admission routes1
Has abstractyes

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