A Study on the Severity of Global Light Pollution Based on Comprehensive Evaluation
Bibliographic record
Abstract
The widespread use of artificial light at night has improved the quality of human life, but it has also brought about a global light pollution problem with profound and complex ecological, human and economic impacts. To measure the severity of light pollution, a new light pollution risk index was developed in this paper. Three main aspects of light pollution impacts were considered: economic level (EL1), ecological level (EL2) and social level (SL), and six tertiary levels were identified under the secondary evaluation indicators. Then, the light pollution risk indices of these four areas were derived from the model using the Tuvaijuituq Marine Reserve in Canada (protected area), Homer City in USA (rural area), Pinggu District in Beijing (suburban area), and Manhattan City in USA (urban area), and the results were compared with the Bortle dark sky scale to test the accuracy of the model and further demonstrate the generality and validity of the LPRI index.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".