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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.746
Threshold uncertainty score0.130

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
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