Origins of natural and anthropogenic sounds in a coastal, seasonally ice-covered, Antarctic marine soundscape
Bibliographic record
Abstract
Baseline characterisation of the acoustic underwater environment is integral to understanding changes in the soundscape of a location. We used passive acoustic monitoring to investigate the soundscape of a seasonally ice-covered, shallow marine environment close to a permanently occupied research station in Prydz Bay, East Antarctica, from July (winter ice cover) 2021 through to February (summer open water) 2022. We applied a suite of automated detectors to detect sounds, with manual analysis of a subset of recordings to validate automated detections and characterise detector performance. From July until late November, the landfast ice cover had a dampening effect on mean daily ambient underwater sound pressure levels. The anthropophony of the ice-covered environment included contributions from aircraft landings and the movements of over-ice vehicles. The biophony was most influenced by the sounds of Weddell, crabeater and leopard seals and Antarctic minke whales. Mean daily sound levels increased immediately as the ice cover decreased and the sea surface became exposed to the effects of wind. The soundscape of the open-water/drifting pack-ice environment then altered to include noise from ship and small boat activities and vocalisations of killer whales and leopard and Ross seals. The results demonstrate a study site with high seasonality in natural sound sources and an unprecedented contribution of noise from human activities during the period of ice cover. There is likely a year-round contribution of anthropogenic noise to the Antarctic coastal marine environment close to research stations that are often co-located with regional hot-spots in faunal occurrences.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".