Analysis and Design of a New Roof Monitor for Day and Night Illumination of a Large Volume Room
Bibliographic record
Abstract
The Illuminating Engineering Society of North America (IESNA) Lighting Handbook recommends illuminance criteria for specific applications to raise the ``minimum standard of care'' required of lighting design to meet human, economic environmental, and architectural concerns. Specific criteria for daylighting of auditoriums (i.e., large rooms) focus on the importance of daylighting integration, control and flexibility, illuminance levels, light distribution, color appearance, and glare. A new skylight design for day and night illumination of a large volume room is proposed. The feasibility of this unique design uses clerestory windows, also known as a roof monitor, and an outdoor lighting array. The design illuminates a room 100-feet wide, 100-feet long, and 75-feet high, located at 40°-north latitude. Comparisons have been made to flat skylight wells for the same volume. The demonstrated design meets lighting criteria that satisfy IESNA standards for day and night to play basketball, a task that requires low contrast and involves small sized objects, like a basketball. The design has been tested with AGI32 software created by Lighting Analysts, Inc of Littleton, Colorado. Results show the new skylight meets IESNA criteria F (for tasks like basketball) during the daytime with an assist by external lighting as the setting sun reduces the daylight.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".