Numerical analysis of underwater sound propagation over the Chukchi Sea shelfbreak
Bibliographic record
Abstract
During the 2015 Canada Basin Acoustic Propagation Experiment (CANAPE) expedition on the southern edge of Canada basin (northern Chukchi Sea shelfbreak), shipboard suspended underwater sound sources were deployed to transmit acoustic signals to hydrophone arrays moored on the deep basin as well as the shallow shelf. In this paper, numerical simulations utilizing the Parabolic Equation method are conducted to provide physical insights into the variability of signals propagating over the shelfbreak and slope and recorded on a vertical array on the Chukchi Sea shelf. The numerical models simulate sound propagating over the slope via the Pacific Halocline duct, which is a water-borne vertical sound duct formed between the layers of Pacific summer water and Atlantic water. The source to receiver distance is about 130 km, and realistic variability is introduced in the numerical models. The previous studies reported in the literature have concluded that the shelfbreak circulation, specifically upwelling, and the sub-mesoscale eddies spun off the shelfbreak jet are the two major causes of the temporal and spatial variability of the Pacific Summer Water Layer over the slope in the region. The numerical simulation study also emphasize on the scattering and attenuation effects caused by ice cover and roughness. A preliminary data-model comparison is made and discussed in this paper. [Work supported by the Office of Naval Research.]
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".