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Record W2913076230 · doi:10.5539/jas.v11n3p289

Effect of Insulator on Thermal Comfort in Poultry House in the Western Region of the State of Paraná

2019· article· en· W2913076230 on OpenAlexvenueno aff
Suélen C. Maino, Jair Antônio Cruz Siqueira, Samuel Nelson Melegari de Souza, Hitomi Mukai, Renata Galvan Rutz da Silva, Carlos Eduardo Camargo Nogueira, José Airton Azevedo dos Santos, M. De Bastiani, Carlos Marques, Janice Reis Ciacci Zanella, Edward Seabra Júnior, Daniel Marcos Dal Pozzo, Alysson Francisco Toscan

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsRelative humidityEnvironmental scienceThermal comfortThermal insulationPoultry farmingMeteorologyGeographyMaterials scienceLayer (electronics)Composite materialForestry

Abstract

fetched live from OpenAlex

This work was carried out with the objective of evaluating effect of different insulation considering two poultry houses. Two dark house type, located in the western region of the State of Paraná, Brazil were analyzed. The poultry house A1 is characterized with trapezoidal aluzinc cover on the upper side interspersed with a layer of polyurethane and aluminized film on the underside, while the poultry house A2 has cover of aluzinc with black tarpaulin. A thermo-hygrometer was used to measure the temperature and relative humidity of the indoor and outdoor air, and a thermal imager was used to collect the surface temperature data of the birds. In this way, it was possible to evaluate the effect of different insulation of poultry houses on birds. Finally, was concluded that the poultry house A1 provided temperature and relative humidity and temperature of the birds closer to those considered as ideal in the literature.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

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 head0.007
GPT teacher head0.213
Teacher spread0.205 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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