Observations of acoustic propagation in the Canada Basin double-duct system from 2016 to 2017 using a 150-km radius tomographic array
Bibliographic record
Abstract
The Arctic Beaufort Sea has a unique double-duct sound-channel capped by seasonal ice cover. A roughly 90-m surface duct (SD) is formed by a river-driven halocline. Below the SD is the approximately 90-m to 250-m depth Beaufort Duct (BD) created by cold Pacific Winter Water sandwiched between warmer Pacific Summer Water and Atlantic Water. A yearlong record (2016-2017) of acoustic propagation measurements in this double-duct system was carried out using a 150-km radius, acoustic tomography array with broadband, 4-hourly transmissions at 175-m depth centered at 250 Hz. Double-duct signal analysis was carried out using a dense-vertical-receiving array spanning the BD. Observations reveal (1) consistent reverse geometric dispersion in the double-duct system with low modes faster than higher modes, (2) distinct first arrival and final cutoff times, and (3) normal dispersion for non-BD/SD modes causing the front to fold back upon itself after the final cutoff. A vertical-wave number spectrogram technique is used to decompose the pulses into an arrival time series for each wave number. Key observables are the first and final arrival travel times, dominant-vertical wave numbers, and signal intensities. Fluctuations are interpreted in terms of the varying stratification, ice cover, and implications for surface heat flux estimation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".