Bibliographic record
Abstract
The basic function of a lighting system is to provide a defined amount of light to a space according to context-appropriate design criteria while minimising energy use. Lighting control strategies based on occupancy and daylight adaptation have been consistently shown to substantially lower lighting energy use compared to fixed systems, and are now ubiquitous in building energy codes. The adoption of light emitting diodes and the integration of information and communication technologies enable lighting control systems to become smarter with a greater integration of sensing, data processing and connectivity, and to evolve into a platform for both lighting and non-lighting applications. We describe different lighting control strategies and their evolution with a focus on commercial office applications. To illustrate emerging approaches, we then discuss two particular smart lighting systems – a wireless, distributed lighting control system and a power-over-Ethernet, centralised lighting control system, with cloud connectivity. The role that a connected lighting system can play in the overall building eco-system is then discussed, and new applications and services that are enabled are presented. Finally, we discuss the challenges to the market adoption of connected smart lighting systems, and the arising opportunities for researchers and practitioners to realise one more round of high-value returns offered by lighting systems.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".