Preliminary estimates of year-round acoustic communications potential in the Canada Basin
Bibliographic record
Abstract
Long-range acoustic communication networks, which operate over hundreds of kilometers, are important enablers for persistent surveillance in the Arctic. Acoustic communication must take place over such long ranges due to difficulty in deploying communication nodes in such an inhospitable environment. Signal transmission over long ranges is challenged by distortion caused by refraction and multipath addition, so the propagation medium should be understood in order to optimize node placement and estimate the network performance. The Arctic is a unique acoustic propagation environment due to the presence of an ice-covered surface and oceanographic variability around the shelf breaks. To better understand the current state of Arctic propagation, acoustic signals were transmitted during the year-long Canada Basin Acoustic Propagation Experiment (CANAPE). In this talk, signals from the CANAPE data are used to assess the feasibility of year-round, long-range acoustic communications in the Canada Basin by examining several metrics. These metrics include: the signal-to-noise ratio (SNR) of the received signals, time-spreading of the signals due to multipath arrivals, and signal coherence. This talk will focus on the seasonality of these metrics in order to assess how the performance of acoustic communication systems may be altered by temporal variability of the Arctic acoustic propagation characteristics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".