The Beaufort Sea acoustic duct's variability and its impact on acoustic propagation using the mode interaction parameter
Bibliographic record
Abstract
The Beaufort duct is a subsurface sound channel formed by cold Pacific Winter Water sandwiched between warmer Pacific Summer Water and Atlantic Water in the Western Arctic Ocean. This duct traps sound waves and allows them to travel long distances without losing energy to lossy interactions with sea ice and surface waves. This study quantifies Beaufort duct variability based on Canada Basin Acoustic Propagation Experiment (CANAPE) and Coordinated Arctic Acoustic Thermometry Experiment (CAATEX) oceanographic observations. Deterministic ocean features induce coupling between acoustic modes confined to the Beaufort duct and non-ducted modes by weakening the duct or causing it to take on an asymmetric form. A non-dimensional mode interaction parameter (MIP) can be defined using the acoustic frequency and the vertical and horizontal scales of sound speed perturbation to characterize coupling strength. It identifies three important wave propagation regimes: sudden approximation, adiabatic approximation, and maximum interaction regime (Colosi and Zinicola-Lapin, 2021). When the MIP between ducted and lossy modes is more and less than 1, strong and weak acoustic variability is predicted, respectively. Variability is high when the MIP between two ducted modes surpasses 1, but modei–mode interference patterns grow more complicated. Acoustic numerical simulations are used to demonstrate various effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".