Broadening the perspective on the acoustic masking effect: a response to comments on Roca et al.
Bibliographic record
Abstract
We are grateful for the thoughtful and pertinent commentaries ( Brumm and Bee 2016 ; Radford 2016 ; Wong and Lowry 2016 ) on our recent analysis of bird and anuran frequency shifts in response to anthropic noise ( Roca et al. 2016 ). We agree with commentators on several highlighted points. First, there is a shortfall of information regarding the effect of anthropic noise masking on acoustic signals produced by animal groups other than birds (i.e., anurans, insects, mammals, and fishes), as well as on nonsexually selected signals (e.g., parental care, alarm calls). Second, beyond studying vocal adjustment mechanisms traditionally invoked to overcome the acoustic mask, research needs to evaluate the potential loss of signal integrity and species fitness implications. This may be achieved by investigating the magnitude of the masking effect on receivers and their strategies to overcome it. Third, there are some problems with the methodology used in several studies. We already acknowledge the lack of consistency in the traits investigated and included in earlier articles. As Brumm and Bee (2016) pointed out, it is important to ensure the reliability and comparability of methodologies, such as those used to measure spectral features. Moreover, specific effort should be devoted to characterize the noise intensity threshold at which different animal species experience noise deterrence effects.
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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 | 0.001 | 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 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".