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Record W2785999408 · doi:10.5539/jas.v10n3p364

Economic Use of Water in Drip-Irrigated Maize in Semi-Arid Region of Brazil

2018· article· en· W2785999408 on OpenAlexvenueno aff
Samuel Silva, Rafaela Félix Basílio Guimarães, Ronaldo do Nascimento, Hallyson De Oliveira, Iêdo Teodoro, José Alberto Ferreira Cardoso, Carlos Vailan de Castro Bezerra, Jaílson Lopes da Penha

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationRandomized block designDrip irrigationSowingProductivityAridEnvironmental scienceYield (engineering)AgronomyCropWater useGeographyMathematicsBiologyEconomicsEcology

Abstract

fetched live from OpenAlex

The objective of this study was to determine the economic level of drip irrigation for the crop of maize in the region of backwoods of Alagoas in Brazil, aiming at a sustainable production and economically viable. For this, the hybrid AG7088 was submitted to five irrigation levels (40, 80, 120, 160 and 200% of ETc) in an experiment developed at the Federal Institute of Alagoas/Campus Piranhas, with a randomized block design and four replications. Harvesting was carried out 98 days after planting, where grain yield with 12% moisture reached 2.1 and 11.8 Mg ha-1 and water use efficiency of 181.8 and 55.3 mm Mg-1 in treatments with 40 and 160% of ETc, respectively. The maximum a physical productivity estimated by the production function was 11.3 Mg ha-1, obtained with 919 mm of irrigation water. The maximum economic yield was 11.1 Mg ha-1, obtained with level of 841 mm (160% ETc).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.221
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2018
Admission routes1
Has abstractyes

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