A STUDY ON THE RELATIONSHIP BETWEEN THERMAL COGNITION OF ROOM SPACE CONTROLLED BY HIGH-TEMPERATURE RADIANT COOLING PANEL COMBINED WITH NATURAL VENTILATION AND HUMAN-BODY EXERGY BALANCE
Bibliographic record
Abstract
This paper discusses the relationship between the indoor thermal conditions provided by a high-temperature radiative cooling system mounted on the ceiling and thermal cognition given by the subjects experiencing its thermal environment in an experimental room simulating common living conditions in residential buildings. The subjects accepted the indoor thermal environment with high relative humidity over 70 % provided that both of the mean radiant and air temperatures kept at 29°C and the air movement exceeds 0.15m/s. We also calculated the human-body exergy balance for indoor thermal conditions that the subjects voted most for “comfort”. Under such a condition, the cool radiant exergy received by the human body was approximately 30mW/m2 and thereby the warm exergy was emitted efficiently from the human body by radiation and convection into the room space. This amount of warm exergy emissions was are larger than that in an air-conditioned room space. Therefore, radiant cooling is not to provide a large amount of cool radiant exergy with occupants, but to provide a sufficient amount in order for the human body to release warm exergy smoothly from the human body surface; that is for spontaneous entropy disposal.
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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 | 0.001 |
| 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) | 0.001 | 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".