The Analysis of Sensory Data from Smart Office Environment Towards the Development of an Intelligent System
Bibliographic record
Abstract
Abstract With the increase in energy prices and the drive to reduce carbon emission, this paper presents an investigation of the use of smart office environments to monitor and evaluate the sustainability and behaviour of employees and the utilisation of space and resources. This paper presents analysis of data in an office environment in a company in Derby city to attempt to understand the behaviour of employees, pattern of work, power consumption and performance of heating and air-conditioning systems. Data from occupancy, room temperature, CO 2 , humidity, lighting, air temperature, windows status are all collected and analysed. The data also included external environmental conditions. The results indicate some correlation between CO 2 levels and the number of employees. They also show correlation between outside and inside environmental conditions. In addition, the utilisation of space was also monitored, and the results demonstrate low utilisation during most days, this was due to Covid-19 and to working from home and off-site patterns. However, the data is found useful to inform future decisions about the actual space needed for normal working conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".