Azimuthal, spatial, and temporal variability of acoustic intensity in cross-shelf direction during the yearlong shallow water Canada Basin Acoustic Experiment 2016–2017
Bibliographic record
Abstract
A yearlong study of spatial, temporal, and azimuthal variability of sound propagation with simultaneously measured oceanography on Chukchi shelf is reported. In a shallow water region, two acoustic sources were deployed for studying the “along” and “cross-shelf” propagation. The “along-shelf” study is presented separately [J. Ascout. Sci. Am. 145 (2019)]. Here, we focus on the “cross-shelf” signal propagation in two frequencies (0.7–1.1 and 1.5–4 kHz) transmitted from a single sound source placed near the sound channel axis in 320 m water depth. Three “cross-shelf” acoustic tracks connected the source and three receiver arrays placed along 50 m isobath. The angle between east most and west most tracks was around 106 deg. Another array at 250 m isobath was deployed along the middle track. Sound emitted from the common source shows different behavior along each track. Concurrently, detailed water column salinity and temperature were measured by environmental arrays in both “along” and “cross-shelf” directions. Sea surface ice was measured by an upward looking sonar for several months. Seasonal injection of different water masses in this region and variations of sea surface conditions (full-ice, transition, and free-surface) are examined using the acoustical oceanographic data. This paper quantifies analysis using correlation between acoustics and oceanographic signals. [Work supported by ONR 322OA.]
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".