Assessing auditory masking for management of underwater anthropogenic noise
Bibliographic record
Abstract
Masking is often assessed by quantifying changes, due to increasing noise, to an animal's communication or listening range. While the methods used to measure communication or listening ranges are functionally similar if used for vocalizations, they differ in their approaches: communication range is focused on the sender's call, while the listening range is centered on the listener's ability to perceive any signal. How these two methods differ in their use and output is important for management recommendations. Therefore it was investigated how these two methods may alter the conclusions of masking assessments based on Atlantic cod calls in the presence of a commercial air gun array. The two methods diverged with increasing distance from the masking noise source with maximum effects lasting longer between air gun pulses in terms of communication range than listening range. Reductions in the cod's communication ranges were sensitive to fluctuations in the call's source level. That instability was not observed for the listening range. Overall, changes to the cod's communication range were more conservative but very sensitive to the call source level. A high level of confidence in the call is therefore required, while confidence in the receiver's audiogram and soundscape is required for the listening range method.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".