Analysis of the ambient noise at the scale of the Bay of Biscay marine sub-region
Bibliographic record
Abstract
The impact of human activities on marine ecosystems has become a major international concern.In the waters of the European Union, the Marine Strategy Framework Directive (MSFD) is in charge of assessing the Good Environmental Status (GES).Based on a 6-year cycle, the GES is assessed through 11 pressure and status descriptors and is supported by data acquisition programs aiming to monitor marine environments.In France, a dedicated monitoring network (MAMBO), focuses on the underwater ambient noise level monitoring, particularly its continuous component related to maritime traffic in two targets frequency bands (one-third octave bands centered on 63 Hz and 125 Hz).A specific algorithm has been developed for background noise estimation without a priori in adverse marine environment.The algorithm enables to deal with complex and non-stationary noises as well as to ensure transient signal rejection.In order to make a proper estimation of the contribution of the man-made ambient noise to ambient noise budget, we quantify the oceanic ambient noise, i.e. without any identifiable source, and determine the traffic close to the monitoring station.A statistical study of the ambient noise is then performed in regard with the environmental conditions in order to analyze meteorological contribution.
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 | 0.001 | 0.001 |
| 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".