Enhancing aging in residential spaces through smart lighting controls technology
Bibliographic record
Abstract
The continuous increase of the older population in modern-day societies (often associated \nwith loss of mobility and reduced independence) has increased the demand for Smart Home \ntechnologies to address and fulfill the needs of elders. One major barrier to the integration of \nSmart Home technologies in residential spaces is the familiarity with the Smart Home \ntechnologies. This research focuses on examining the willingness of people to age in place and \nthe effectiveness of Smart Lighting control technologies as a Smart Home technology within \nresidential spaces for those who seek to age in place. The data for this study was collected \nthrough interviews and online surveys from the targeted participants who were 55 years of age \nand older at the time of the interview/survey and residing in Canada and the U.S. A comparative \nstudy was conducted between the response of Smart lighting and non-Smart lighting users to \nexamine whether the integration of Smart lighting technologies into residential space can make \naging in place easier. The findings of the study suggest that Smart Lighting systems controlled \nthrough smartphone applications and smart speakers within residential spaces can increase \npeople’s comfort and enhances the aging in place practice.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".