Sea observatories and acoustic events: Towards a global monitoring of ocean noise
Bibliographic record
Abstract
The next decades will see increasing levels of offshore industrial development that will lead to increased amounts of noise pollution in the oceans. Amongst these developments, shipping, oil and gas prospection, navy exercises as well as offshore windmills are already playing a leading role in introducing considerable amount of noise in an increasing number of areas. Underwater sound sources produced by these latter activities present the highest intensity amongst those anthropogenically generated in the sea, reaching more than 230 dB re 1 uPa at 1m from the source. These sounds can have physical, physiological and behavioural effects on the marine fauna in the area of action: mammals, reptiles, fish and invertebrates can be affected at various levels depending on the distance to the sound source. Marine mammals could be one of the more sensitive groups of marine species because they have a highly developed auditory system and use sound actively for feeding and for social communication. It is also known that marine mammals are vulnerable to the effects of habitat loss or reduced survival and reproduction rates. The problem faced by the offshore industry, and more generally by the society, is that many economically important activities at sea are at risk because of a lack of information about the effects of anthropogenic sound on marine mammals and especially a lack of available tools to mitigate these effects. The challenge here is to implement technological developments that combine the interests of the industry and the good environmental status of the oceans. Based on the existing technology successfully implemented at underwater observatories worldwide (European Sea-floor Observatories Network of Excellence, ESONET, European Member States; ANTARES, France; NEPTUNE, Canada; Kushiro, Japan) by the Laboratory of Applied Bioacoustics of the Technical University of Catalonia (LIDO, Listen to the Deep-Ocean Environment, http://listentothedeep.com), a real-time passive acoustic monitoring solution is available to monitor ocean noise at large spatial and temporal scales. The LIDO acoustic detection, classification and localization (DCL) system provides continuous information on noise levels and allows the comparative monitoring of noise trends in very diverse geographic locations, as well as relates the presence of key marine species with changing ocean noise.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".