Understanding the association between urban noise and nighttime light in China
Bibliographic record
Abstract
Nighttime light data partially reflects the process of urban modernization and its reaction to urban scale, but its correlation with noise remains unclear, especially over a long-term time series, remains unclear. To address this gap, we examine 31 provincial capital cities and municipalities in China as the study area, utilizing noise monitoring data and nighttime light data to explore their relationship in urban areas from 2012 to 2021. The results show that: (1) During the study period, the regional environmental noise and night light index of 31 major cities exhibited a consistent upward trend, with the average equivalent sound level of environmental noise in most urban areas fluctuating between 50.1 dB (A) and 60.0 dB (A). The overall quality evaluation was average, indicating that the effectiveness of noise pollution prevention and control was difficult to sustain in the long term. (2) The driving factor affecting the change in environmental noise quality in urban areas is population density. (3) There is a significant correlation between different types of noise proportion and urban construction and development, with the closest correlation observed with the urban economy, urban built-up area, and urban construction land area. Cities with high levels of urbanization tend to have more vulnerable acoustic environments. (4) Industrial noise percentage of urban environmental noise has a significant positive impact on nighttime light data. This study provides valuable insights for promoting the coordinated development between urbanization and acoustic environment optimization, as well as fostering a healthy and sustainable living 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".