Demonstration of Underwater Channel State Information Acquisition in Grand Passage, Nova Scotia
Bibliographic record
Abstract
This article presents a channel state information acquisition approach based on a Markov chain process that exploits information from the physical environmental conditions, including the tide phase and flow. The method is intended to predict channel characteristics, including the gain, delay, and Doppler spread, as well as the standard deviation of intrapath delays in time-varying conditions. Specifically, the correlation between different oceanic processes and the acoustic channel characteristics is confirmed to define a set of tide-dependent states corresponding to a particular channel condition. Channel soundings from a 34-day sea trial conducted in Grand Passage, Nova Scotia, are used to derive the channel characteristics statistics. For this purpose, channel soundings measurements are applied to a parametric model of the propagation channel. The probabilistic parametric model forms a data set by characterizing the time-varying channel impulse response and describing the channel tapped-delay structure statistically as a function of different tide phases. The proposed Markov chain is driven by the measured channel data set and predicts the future channel characteristics one tide cycle ahead. To validate the accuracy of the proposed method, the predicted channel characteristics are compared to the channel measurements obtained in a 566-m channel in Grand Passage, Nova Scotia.
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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 | 0.001 | 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".