Environmental and Behavioral Modeling of Mitigating Light Pollution: Implication for a Better World
Bibliographic record
Abstract
Light has always fascinated humans, thus they used to burn different materials to illuminate their indoors and outdoors as well at night for better visibility and safety. Nighttime lighting has rapidly proliferated throughout the cities and has kept the darkness away. Artificial light, which has become a necessity, contributes to the so-called Light Pollution. Light pollution is therefore a crucial aspect for lighting designers to consider while designing. Knowing how to accomplish ‘good’ lighting is considered a characteristic, and a distinctive skill of lighting professionals. The objective of this study is to highlight the causes behind light pollution, the groups that are contributing to its existence, and to identify ways and means for avoiding it. To achieve the objective of this study, the following three questions were adopted: What produces light pollution? How could light pollution be reduced? Finally, are lighting design companies helping in light pollution mitigation? A survey of lighting professionals was adopted to trace out the main causes of light pollution. The results show that lighting professionals are considered as the main responsible factor for light pollution, and play a major role in its mitigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".