Anthropogenic noise events perturb acoustic communication networks
Bibliographic record
Abstract
Anthropogenic noise sources impact ecological processes by altering wildlife behavior and interactions with cascading impacts on community structure. The distribution and magnitude of such noise has grown exponentially over the past century, and now inundates even remote areas. Here we investigate biological responses to prolific, anthropogenic noise sources associated with the physical presence of the source (vehicle noise and human voices) and disconnected from it (aircraft overflight). Bioacoustic responses to these noise sources were documented at 103 sites in 40 U. S. National Park units. The presence of bird sounds was noted in 10-s audio samples every 2 min, for 8 days at each site and related to the presence of human voices, vehicle noise, and aircraft noise in the same and preceding samples. Generalized additive models were used to fit smoothing splines to weight the influence of noise in past samples on the probability of detecting bird sounds in the present sample. We found that the probability of hearing birds increased immediately following noise events, and decreased about 2 h after the event. The negative effects were persistent more than 3 h after a noise event. The persistence of these responses – especially for noise from jets that were many kilometers distant – raises questions about the functional significance and ecological consequences of this altered activity, particularly in light of the widespread and diverse habitats in this study and ubiquity of the noise sources evaluated.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| 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".