Comment on 'Simulation of annual daylighting profiles for internal illuminance' by John Mardaljevic
Bibliographic record
Abstract
Dr. Mardaljevic's recent paper on his implementation of the concept of daylight coefficients into the RADIANCE simulation environment is a well-written and a logical extension of his previous work on the validation of RADIANCE daylight simulations under real sky conditions. The interested reader might further want to learn, that the computational efficiency of Tregenza's concept of daylight coefficients has been recognized by a number of researchers, and the calculation approach has been implemented into RADIANCE by at least two other groups; one version has been implemented into the building simulation program ESP-R by Janak and Macdonald at the University of Strathclyde in Glasgow, Scotland and another version has been developed by Reinhart and Herkel at the Fraunhofer Institute for Solar Energy Systems in Freiburg, Germany. Both approaches also distinguish between direct and diffuse daylight coefficients although some differences exist in how direct sunlight is treated.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".