Beamforming shallow water propagation of mid-frequency acoustics on the Chukchi Shelf
Bibliographic record
Abstract
The shallow water Canadian Basin Acoustic Propagation Experiment (CANAPE 2016–2017) was designed to study the effect of oceanographic variability on the acoustic field in the Arctic. The physics of the acoustic waveguide on the Northeastern edge of the Chukchi Shelf are influenced by dynamic boundary conditions and spatio-temporal fluctuations in temperature/salinity profiles, including the upwelling of Atlantic bottom water, sinking Bering Sea surface water and sub-mesoscale eddy formation. These fluctuations influence the acoustic waveguide characteristics of a persistent sound speed channel centered at 150 m depth. The University of Delaware (UDel) deployed seven oceanographic moorings (OM) perpendicular to the isobaths on the Chukchi Shelf (145–700 m depth). A Naval Research Lab source (S1) was deployed in-line with the OMs, generating Linear Frequency Modulated (LFM) signals in alternating 700–1100 Hz and 1400–4000 Hz bands. A Vertical Line Array (VLA) was deployed in-line and within the span of the OMs. The array aperture spanned the depth of the sound speed channel. Beamforming measurements from the VLA in combination with environmental measurements and 2D PE model output will determine the degree of influence of individual physical oceanography processes on the spatio-temporal structure of the sound channel and internal acoustic wave propagation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".