Under-ice ambient noise and its masking effect on marine mammals in the Arctic
Bibliographic record
Abstract
Climate change is changing the pattern of sea ice in the Arctic, which increases underwater ambient sound levels during the ice-covered season. The Arctic is home to multiple marine mammal species, even during the winter, which rely on the underwater acoustic environment for acoustic communication. Thus, climate change may be impacting the ability of these animals to communicate. Under-ice ambient noise is primarily generated by wind acting on the ice surface, rapid temperature changes, and impulsive transient signals resulting from ice cracking and ridging. To characterize under-ice ambient noise, acoustic data were collected from several locations in the western Canadian Arctic in this study. To determine the relationship between the received noise level, and wind speed and ice concentration, correlation analysis was used. For the detection and analysis of broadband transient events in the acoustic data, a spectrogram image processing approach was used. The spectral characteristics of transient events were generated using measurements and a random pulse train model. Furthermore, the wind and ice generated noise components were combined to predict the total ambient noise field in an ice-covered ocean using a wavenumber integral model. Finally, the model together with measurements was used to assess how ice-generated noise can cause communication masking in marine mammals such as ringed seals and bearded seals.
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 | 0.000 | 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) | 0.001 | 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".