Natural variations in underwater noise levels in theEastern Grand Banks, Newfoundland
Bibliographic record
Abstract
“The topic of underwater noise pollution due to oil & gas exploration is a genuine con- cern to scientists and researchers. Studying acoustic propagation from noise sources has become one of the standard environmental impact assessment criteria for offshore devel- opments. Lower level noise is also a concern when persistent and higher than naturally occurring background noise. The natural environment contributes sound through wind and wave motion, currents, precipitation, and sea ice. A two-month autonomous acous- tic monitoring program was conducted in 2015 on the Grand Banks, as part of a study to understand the impact of seismic surveys off the coast of Newfoundland. This study aims to use that data to improve our understanding of the ambient soundscape and relate the observations to known relationships between noise levels and wind and rainfall rates. A challenge with the present data is that the observations are made in shallow water where bottom and surface reflections act to increase expected natural sound levels. This increase in sound levels interferes with algorithms used to relate wind and rain to noise levels. An analytical model was used to adjust noise levels accounting for the shallow water envi- ronment. The corrected data were evaluated using algorithms for Weather classification developed by Nystuen which were used to identify the data points with sound levels asso- ciated with shipping, drizzle, rain and near surface bubbles. The shipping contamination was removed from the data sets and resulting sorted data was used to estimate wind speeds that were compared to independent observations obtained from model data provided by Department of Fisheries and Oceans, Canada.”
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 | 0.001 | 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) | 0.002 | 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".