Insights on the effect of aircraft traffic on avian vocal activity
Bibliographic record
Abstract
Aircraft noise is pervasive across the USA, including in national parks, but its effects on wildlife remain unresolved. As with other noise sources, aircraft noise may affect species physiology and behaviour by being perceived as a threat, distracting individuals, or degrading the sensory environment. This study aimed to understand the effect of aircraft traffic and associated noise on the richness of bird vocalization activity in a remote national park in the USA. We used a continent‐wide acoustic dataset encompassing over 30:00 h of annotated recordings to identify two geographically similar sites with high rates of bird vocalizations and both high and low rates of aircraft noise. We selected sites in Denali National Park, both of which experience little human presence, and quantified the richness of bird vocalizations before, during and after aircraft events. We present evidence of a community‐level behavioural response to aircraft noise, with increased bird vocalization richness after aircraft events at a site with relatively lower aircraft noise. At the site with low rates of aircraft noise, we found bird vocalization richness did not significantly change during an aircraft event but did increase after an aircraft event. At the site with high rates of aircraft noise, bird vocalization richness did not significantly change during or after an aircraft event. This study provides new insights into wildlife responses to aircraft traffic and associated noise and highlights the importance of noise research in the management of relatively quiet and undisturbed landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".