Trends of Ocean Underwater Acoustic Levels Recorded Before, During, and After the 2020 COVID Crisis
Bibliographic record
Abstract
Since the Industrial Revolution, underwater soundscapes have become more complex and contaminated due to increased cumulative human activities. Anthropogenic underwater sources have been growing in number, and shipping noise has become the primary source of chronic acoustic exposure. However, global data on current and historic noise levels is lacking. Here, using the Listening to the Deep-Ocean Environment network, we investigated the baseline shipping noise levels in thirteen observatories (eight stations from ONC Canada, four from the JAMSTEC network, and OBSEA in the Mediterranean Sea) and, in five of them, animal presence. Our main results show yearly noise variability in the studied locations that is not dominated by marine traffic but by natural and biological patterns. The halt in transportation due to COVID was insignificant when the data were recorded far from shipping routes. In order to better design a legislative framework for mitigating noise impacts, we highlight the importance of using tools that allow for long-term acoustic monitoring, automated detection of sounds, and big data handling and management.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".