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Record W3084006022 · doi:10.5539/jas.v12n10p162

Optimization of Energy Efficiency and Environmental Comfort in Broiler House

2020· article· en· W3084006022 on OpenAlexvenueno aff
Moacir José Dalmina, Jair Antônio Cruz Siqueira, Carlos Eduardo Camargo Nogueira, Samuel Nelson Melegari de Souza, Luciene Kazue Tokura

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectural engineeringVentilation (architecture)Energy consumptionEfficient energy useStudioNatural ventilationRelation (database)Energy conservationEngineeringComputer scienceSimulationMechanical engineeringTelecommunicationsElectrical engineeringDatabase

Abstract

fetched live from OpenAlex

The objective of the study was to develop a design methodology involving a mixed system for better use of natural lighting and ventilation, together with electrical heating and ventilation systems that are currently used in commercial aviaries. Two building models were analyzed, one open conventional, and the other developed specifically for this study, with a cross ventilation brise-soleil system that provided greater energy efficiency in aviaries. Subsequently, the two models were compared using Autodesk’s Revit software through the Green Building Studio, to analyze the energy consumption of buildings during the year. The results showed that the model of poultry developed for the study proved to be more efficient in relation to the model of open poultry. The proposed broiler house was 21.07% more efficient than the conventional open aviary.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.011
GPT teacher head0.186
Teacher spread0.175 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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