Adaptive outdoor comfort model calibrations for a semitropical region
Bibliographic record
Abstract
This paper is a part of a research project funded by Architectural Research Centers Consortium (ARCC) and Florida Atlantic University (FAU). The project focuses on finding a way to assess outdoor comfort and developing design criteria for a semitropical region of South Florida. A series of surveys has been conducted in the summer and fall seasons to obtain participants’ sensation votes corresponding to recorded climatic parameters. More data need to be gathered for the calibration and validation. This paper attempts to evaluate different models using the survey data, and identify strong candidates for further study. The models are all based on Predicted Mean Vote (PMV), an index traditionally used to assess indoor comfort. Results from the PMV equation exhibits a promising trend, but needed some adjustments. Calibration alone cannot improve its prediction. After some computational experiments with different adjustment strategies, five model candidates exhibit high rates of agreement with Actual Sensation Votes (ASV). Adaptive and separated calibration approaches applied to the PMV compensate participants’ adaptation to the outdoor condition. They improve the PMV’s prediction considerably.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".