Does Fan Feel and Visibility During Thermostat Interaction Affect Temperature Selection in Warm Ambient Conditions?
Bibliographic record
Abstract
Abstract A vicious cycle exists when higher global temperatures increase the demand for indoor air-conditioning, which consumes significant energy while heating the outdoors. These higher outdoor temperatures then prompt more air-conditioning use. This unsustainable cycle motivated us to develop an intervention to encourage more energy-efficient temperature adjustments during warm ambient conditions. We explored whether an experimental thermostat interface, which incorporated mechanical fans, affected individual thermostat-setting behavior. Experimental parameters were (1) feel versus don't-feel fans and (2) high- versus low-visibility fans. Participants were 23 university students, including 20 enrolled in an introductory psychology course. When prompted to make temperature adjustments, we found that participants who felt the fans selected higher (more energy-efficient) temperatures in warm ambient conditions. This effect held regardless of whether participants could clearly see the fans or not. These results inform how products can be designed to increase energy-conscious behaviors.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".