Validation of skylight performance assessment software
Bibliographic record
Abstract
This paper presents a comparison study between the simulations of SkyVision and measurements made in a scale model. SkyVision is a computer tool to calculate the overall optical characteristics, indoor daylight availability and energy saving potential of projecting and tubular skylights. The measurements included the overall visible transmittance and indoor daylight illuminance of the skylight. A rectangular wooden box was used as a scale model of a simple commercial building. The top surface of the box was fitted with a curbed opening to accommodate the skylights to be tested. Seven skylight shapes were tested: two circular dome models, one with clear and one with white acrylic glazing; two rectangular bubble dome models, one with clear and one with white acrylic glazing; a clear acrylic hexagonal pyramid model; a clear polycarbonate barrel vault model; and a tubular skylight model. The measurements were conducted for a whole day period, thereby covering different sky conditions: overcast, partly cloudy and clear sunny skies. SkyVision's simulations for the skylight transmittance compared reasonably well with the actual measurements, except for the hexagonal pyramidal skylight. The hexagonal pyramid surface exhibited some lens effects around the surface vertices, which were not possible to model in SkyVision. As for the skylight indoor illuminance comparison, the SkyVision's simulations were in good agreement with the actual measurements for all occurrences of sky conditions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".