Thermal Environment and Animal Comfort of Aviary Prototypes with Photovoltaic Solar Panel on the Roof
Bibliographic record
Abstract
The areas on the roofs of animal production facilities present great potential for generating solar energy. However, the impact that the addition of new material on the roof can generate on the installation’s thermal environment is still poorly studied. Thus, this study aims to investigate the effect of the application of photovoltaic panels in the roofs of prototypes, in reduced-scale aviaries, on the thermal environment, and on the animal comfort condition inside the prototypes. For this, six prototypes of aviaries on a reduced 1:5 scale are used. They are equipped with three types of tiles (ceramic, fiber-cement, and metal), with and without a photovoltaic panel. The effect of applying the photovoltaic panel is verified by evaluating the air temperature, the surface temperature of the roofs, the temperature and humidity index (THI), the black globe humidity index (BGHI), and the radiation heat load (RHL). The results show that applying the photovoltaic panel on the roof, regardless of the type of tile, is efficient in reducing the air temperature by about 0.4 °C, the BGHI by about 0.7, and the RHL about 4 W/m2. As for THI, there is only a 4.8 reduction in fiber-cement roofs.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".