Imagining the future of office lighting: smart, sustainable, solid-state
Bibliographic record
Abstract
This is a time of revolutionary change in the lighting industry. Solid-state lighting (SSL) systems are on track to achieve lighting efficiencies for general lighting in excess of 150 lumens per watt (lm/W) in the not-too-distant future (see http://www1.eere.energy.gov/buildings/ssl/). This is double the typical performance of the ubiquitous linear fluorescent systems in use today. The total system performance could be further improved by the addition of smart controls, which include occupancy sensing and daylight harvesting, among other features. These controls are easier to implement with SSL systems because of SSL’s digital nature. Interestingly, SSL systems also offer other new functions for interior lighting that have yet to be fully explored. Having started in 2008 to consider what these new functions might be, and how they might be used, our team at the National Research Council of Canada is convinced that further development of these concepts will lead to greater energy savings for lighting, faster adoption of SSL lighting technologies, and improved lighting quality. This summary shows what we think in 2012 that the future will hold
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".