Light pollution evaluation system based on combination weighting method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Light pollution is often overlooked, but according to statistics, it has increased by at least 49% globally in the past 25 years. In this paper, the Combination weighting method, metabolic GM (1,1) model, and other methods are used to study the light pollution problem. A light pollution risk level evaluation system is established by using the combination weighting method and several indicators related to light pollution. Based on the analysis of samples from China and the United States, a range of light pollution control strategies is proposed encompassing three key aspects: electricity accessibility, population density, and biodiversity coverage. The light pollution situation of the two locations in the upcoming year is predicted and compared using a combined approach of the metabolic GM (1,1) model, considering various strategies as well as no strategy implementation. Ultimately, it can be seen that the strategy of electricity accessibility is more effective. The establishment of a light pollution evaluation model enables the measurement of the effectiveness of prevention and control strategies, thereby enhancing the ability to effectively manage light pollution.
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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 | 0.003 | 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 | 0.000 |
| 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 it