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Record W2952569126 · doi:10.5539/jas.v11n9p198

Reduction of Production Costs in Agribusiness Companies in the Western Region of the State of Paraná Using Photovoltaic Systems Connected to the Grid

2019· article· en· W2952569126 on OpenAlexvenueno aff
Mario Elias Carvalho do Nascimento, Jair Antônio Cruz Siqueira, Carlos Eduardo Camargo Nogueira, Maritane Prior, Maurício Guy de Andrade

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemAgribusinessCost reductionProduction (economics)ElectricityProfit (economics)BusinessGridAgricultural scienceEnvironmental economicsProduction costAgricultural economicsAgricultureOperations managementAgricultural engineeringEnvironmental scienceEngineeringEconomicsMarketingElectrical engineeringMathematicsGeographyMicroeconomics

Abstract

fetched live from OpenAlex

This investigation was a case study of designs for grid-tie photovoltaic systems in agribusiness enterprises in the western region of the state of Paraná, in Brazil. Electricity consumption data of three grain storage enterprises and a complex with three aviaries was studied. Their monthly electricity consumption and production costs data were collected, and solar radiation data for the same locations were collected from the SunData database. Indices that related electricity costs and production expenses, and the photovoltaic system costs and production expenses were developed. The indices showed to be efficient tools of cost relation for systems of different installed powers and different agricultural productions. The study showed a cost reduction in production of up to 62.71% for the aviaries, and 76.87% for the grain storage enterprises, concluding that the use of grid-tie photovoltaic systems reduces the cost of production and increases the profit of the enterprises.

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)0.002
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 score0.775
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.231
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

Citations1
Published2019
Admission routes1
Has abstractyes

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