Energy Consumption Evaluation in Dark House Aviaries, With and Without Thermal Insulation: A Case Study in the Western Region of the State of Paraná
Bibliographic record
Abstract
Broilers production suffered, due to major investments in technology and genetic development in recent decades, to adapt to the conditions and requirements of the national and world market. Studies on the production systems of reraring show the importance of the economy in the sector, which is one of the most competitive in the agribusiness sector. The present work aimed at studying two dark house aviaries construction, with and without thermal insulation, to evaluate the variations in temperature, gas consumption, and effect of the insulation. Both facilities were checked for temperature variation, using J-type thermocouple sensors and recording in datalogger; LPG consumption, and the production performance considering mortality of birds, feed consumption, water intake, and weight gain. It was concluded that the Dark House with Thermal Insulation (DHTI) aviary showed better broiler birds performance indexes, lower mortality, and greater weight gain. But when it comes to performance in the face of temperature conditions, the Conventional Dark House (CDH) aviary showed values better suited to the production, as noted in the bibliographic references.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".