Disentangling effects of noise from presence of anthropogenic infrastructure: Design and testing of system for large‐scale playback experiments
Bibliographic record
Abstract
ABSTRACT Anthropogenic noise may be detrimental to many bird species, and manipulative experiments would help us understand these effects. We present the design and validation of a self‐sustaining solar‐powered broadcasting system that will allow researchers to disentangle acute or chronic effects of noise from effects of the presence of infrastructure, even in remote areas. We tested the broadcasting system using a case study on noise from oil well infrastructure in southern Alberta, Canada. Recordings from 2 types of oil wells were obtained and then broadcasted through 6 independent playback units continuously for a period of 3 months in 2013, at sites undisturbed by oil development. Sound measurements at real oil wells and at sites with broadcasting systems simulating the noise from these oil wells demonstrated that real and projected noise had very similar sound pressure levels, attenuation trends, and spectral composition. Throughout this long‐term playback experiment, the system produced power and noise reliably and consistently. In conjunction with bird surveys, this experimental design and infrastructure can be used to allow researchers to dissociate the presence of anthropogenic development from associated noise, providing us with information that will help decrease the environmental impacts of human activities. © 2015 The Wildlife Society.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".