Acoustic propagation from the Canadian Basin to the Chukchi Shelf
Bibliographic record
Abstract
The Pacific Arctic Region, encompassing the Bering, Chukchi, Western Beaufort, and Eastern Siberian shelves and seas, has experienced decadal changes in atmospheric conditions, seasonal sea-ice coverage, and seawater temperature. In the summer of 2016, the Canada Basin Acoustic Propagation Experiment (CANAPE) was conducted to understand the changing soundscape and to explore the use of acoustic signals as a remote sensing tool in the modern Arctic. During the experiment, low-frequency signals from five tomographic sources located in the Canada Basin were recorded by a short vertical line array of hydrophones deployed from a research vessel. The recordings were made at seven stations located in the Canada Basin, on the continental rise, and on the Chukchi Shelf. The propagation distances ranged from 50 km to 500 km, and the propagation conditions changed from ducted by the Beaufort Lens in the basin to upward refracting on the continental shelf. Multiple measurements of the sound speed profile were acquired at each station to characterize the temporal and spatial variability of the sound speed field. This talk examines the range-dependent measurements and explains the observed variability in the received signals through propagation modeling. [Work sponsored by ONR.]
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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".