The relationship between anthropogenic light and noise in U.S. national parks
Bibliographic record
Abstract
Abstract Context Natural sound and light regulate fundamental biological processes and are central to visitor experience in protected areas. As such, anthropogenic light and noise have negative effects on both wildlife and humans. While prior studies have examined the distribution and levels of light or noise, joint analyses are rarely undertaken despite their potentially cumulative effects. Objectives We examine the relationship between different types of anthropogenic light and noise conditions and what factors drive correlation, co-occurrences, and divergence between them. Methods We overlaid existing geospatial models of anthropogenic light and noise with landscape predictors in national parks across the continental U.S. Results Overlapping dark and quiet were the most common conditions (82.5–87.1% of park area), representing important refuges for wildlife and human experience. We found low correlation between anthropogenic light and noise (Spearman’s R < 0.25), with the exception of parks with a higher density of roads. Park land within urban areas had the highest probability of co-occurring high light and noise exposure, while park areas with divergent light and noise exposure (e.g., high light and low noise) were most commonly found 5–20 km from urban areas and in parks with roads present. Conclusions These analyses demonstrate that light and noise exposure are not always correlated in national parks, which was unexpected because human activities tend to produce both simultaneously. As such, mitigation efforts for anthropogenic light and noise will require efforts targeting site-specific sources of noise and light. Protecting and restoring sensory environments will involve constructive partnerships capable of reconciling diverse community interests.
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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 | 0.000 | 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) | 0.001 | 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".