Integrating multiple disciplines to understand effects of anthropogenic noise on animal communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Anthropogenic noise is pervasive and may affect wildlife in many ways. Anthropogenic noise also adds to the acoustic environment's complexity, making it more difficult for animals to detect and discriminate among important signals. By integrating knowledge gained from research in experimental psychoacoustics, psychophysics, and neurophysiology into applied ecology, we can refine our understanding of the impacts of anthropogenic noise on wild populations. A multidisciplinary approach is particularly important for understanding signal perception, masking, auditory scene analysis, multimodal communication, and cross‐modal interference. We demonstrate the benefits of using knowledge gained from a variety of different disciplines to understand masking effects of anthropogenic noise using our research on effects of petroleum infrastructure on grassland songbirds. Incorporating knowledge from diverse disciplines and involving several taxa, including humans, can help inform ecological conservation and management practices, and has the potential to help researchers generate novel and effective mitigation measures to counter negative effects of noise.
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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) | 0.000 | 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 it