Evaluating Light Pollution: An IES Model for Intervention Strategies
Bibliographic record
Abstract
There is an increasing urgency to address how the light pollution risk level can be accurately and comprehensively measured and evaluated. Based on current research and data, this paper proposes a model concerning light pollution risk levels applicable to various regions. Optimized intervention strategies are then provided to reduce the effect of light pollution. For one thing, this paper establishes an Illumination-Environment-Society Evaluation (IES) model to evaluate a region’s light pollution risk level. Primary indicators of the model involve three dimensions, each quantified by 2 to 5 secondary indicators, with sufficient data analysis conducted, including data rasterization of satellite remote sensing images, K-means clustering analysis, Principal Component Analysis (PCA), Entropy Weight Method (EWM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), and other assistant algorithms. In this regard, the present study obtains and grades some regions’ light pollution risk levels. For another, this paper determines three possible intervention strategies for light pollution based on the IES model after interpreting the results. Non-linear programming methods are also employed to optimize these three strategies. The present study aims to exploit a new avenue for relevant environmental research, providing references for light pollution measurement and intervention.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".