Underwater glider localization using broadband source transmissions in the Canada Basin
Bibliographic record
Abstract
Two Seaglider autonomous underwater vehicles were deployed as mobile receiving platforms in a 2016–2017 tomography experiment in the Canada Basin. The introduction of gliders into a tomography experiment has the advantage of adding acoustic data receptions at many depths and ranges relative to moored acoustic sources. The challenge in interpreting these data is the lack of statistics at any given location and the uncertainty in the glider position during a dive, the latter resulting in a fundamental ambiguity between the position and sound speed. The acoustic arrival matching localization technique, performed in post-processing, has been employed to position the instruments while underwater using transmissions from moored acoustic sources. This technique capitalizes on the broadband nature of the tomography sources and estimates latitude and longitude while relying upon the vehicle’s pressure sensor for depth. Acoustic ranging was also performed onboard the gliders during the deployment, and estimated ranges from the moored sources were transmitted via Iridium satellite link every few hours when the glider surfaced. Positions resulting from acoustic arrival matching localization will be compared with results based on semi-real time acoustic ranging. Results will be discussed in the context of reviewing the state of the art in underwater glider localization.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".