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.
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 | 0.029 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.051 | 0.077 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".