Measuring the Thermal Comfort and the Sound Level in Design Studio Classes in Architecture Engineering Colleges
Bibliographic record
Abstract
Thermal comfort is one of the most important topics in the course Environmental Control, ARC404 assign to Architecture program in the college of architecture Engineering and Digital Design, 6 students in this course will share in the research, will distribute in three groups. This research is aiming to let the students learn and practice how to measure the thermal comfort in-studio classes focusing on the temperature, the humidity, and the noise, analysing and find solutions. The methodology of the research is based on using monitor devices; noise level smart meter, smart temperature and humidity measurement meter with data analysis by using Excel computer program as well as, distrusting a survey to know the user’s opinion. The college has three types of a studio class, one facing the courtyard, with large glass window, the second at the middle of the corridor with high-level window, the third one is far away from the courtyard without any window. The results compared by The United Nation Environut Protection Agency (EPA) noise levels. The results show that the studio class CBC09 level of sound exceed 60 dB which consider as noise. Also, the class CBC01 is the most comfortable class because of 25 C° temperature, 40% humidity and 55 DB the sound level is also exceed the limit by EPA. The conclusion of the research paper will highlight some scientific solutions in walls, ceiling and floors for the studio classes to be applied in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".