A Comparative Study: The Benefits of a Novel Illuminance Calculation Method over Luminance Calculation Method for Optimal Roadway Lighting Design Applications
Bibliographic record
Abstract
Abstract This paper discusses the challenges and impacts of the luminance calculation method for roadway lighting applications and recommends a novel illuminance calculation method as the most suitable for Roadway Lighting Applications. This change would reduce the costs of roadway lighting design without increasing risks, which will benefit society and the profession. Luminance calculations are complicated and take approximately five times longer than the new illuminance calculation method, provide no additional insight, and result in the same design outcome. This paper makes a comparative study and analyzes both methods using regular comparative assessment and quantitative and qualitative assessments and provides a solution to the over 50-year-old challenge. The quantitative evaluation uses a sample case study and examines its corresponding benefit-cost ratio. The qualitative approach is to complete a survey among the peers and the lighting designers. This is the first paper to address all these parameters of roadway lighting holistically. This paper will be helpful for academics, researchers, scientists, engineers, consultants, architects, lighting designers, contractors, developers, financial institutions, and government agencies funding outdoor lighting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".