Discovery of Threshold Visibility Limit (TVL) Using the Number of Photons Intruding into the Human Eye and Possible Application to Outdoor Lighting
Bibliographic record
Abstract
This chapter addresses the threshold visibility limit (TVL) using the number of photons intruding into the Human Eye. Understanding the human visual system's nighttime performance is essential for designing safe lighting for vehicular and pedestrian traffic in nocturnal outdoor built environments. Although assessing visibility is a complex process, the current knowledge of how the eye receives and processes light and how well it can distinguish objects at various lighting levels and distances is important. The current approach for calculating outdoor lighting level requirements is complicated and limited to illuminance and luminance measurements and does not allow designers to evaluate the threshold visibility. This paper aims to develop an outdoor lighting visibility calculation using the photons per second calculation method. This novel calculation method is applied to the candlelight's visibility distance and the milky way's Vega star visibility to validate the calculation approach. The luminance calculation has many shortcomings and assumptions, including the fixed observer position and viewing angle, and the results do not correlate with reality. It is complex and difficult to assess or reach a consensus since millions of colors and luminances contrast anyone can witness in the environment. The quantitative assessment uses mathematical calculations to find the TVL and visibility distance of candlelight and vega star visibility from the Earth's surface. The comparative assessment method verifies and confirms the candlelight visibility distance. This is the first paper to find the TVL so that any future lighting application TVL can be evaluated, and the required visibility level determined for a particular vehicular and pedestrian traffic safety in nighttime outdoor environments. This paper will help researchers, scientists, engineers, consultants, architects, lighting designers, and government agencies seeking to improve outdoor lighting for safety, health, well-being, and quality of life in the built environment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".