Using Core Sunlighting to Improve Illumination Quality and Increase Energy Efficiency of Commercial Buildings
Bibliographic record
Abstract
The lighting industry has been challenged to deliver high quality illumination while using less electrical energy and, to achieve this, it is generally agreed that it is a good idea to incorporate daylighting strategies. We have developed a new system that is capable of delivering sunlight deep into the core of multi-floor buildings. This system has the potential to reduce the energy required for illumination in standard commercial buildings by at least 25%, reduce peak electrical power demand when it is needed, and provide high quality illumination with excellent color rendering characteristics. This result is achieved by delivering sunlight to the interior regions of the building, thereby replacing electric lighting on average 75% of the time each day that the sun shines within six core daylight hours. As a result of both its cost-effectiveness and the ease with which it can be integrated into standard building construction, this is the first core sunlighting system with potential for widespread adoption. An initial demonstration shows a substantial reduction in the electrical lighting load and enables an evaluation of the spectral quality of the sunlight illumination from the perspective of the occupants.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".