Identifying "Skylites" for AUV operations under pack ice: insights from ice-draft profiling by moored sonar
Bibliographic record
Abstract
The past decade has seen a remarkable evolution of sea-floor-based ice-draft profiling capabilities. Efforts have progressed from an original Beaufort Sea deployment of a single upward-looking acoustic echo sounder to the almost routine present-day positionings of special-purpose profiler units which operate in conjunction with adjacent current profiling and ice drift measurement instruments. These units allow detailed specification of draft statistics and high resolution mapping of moving ice undersurfaces for both on-board storage and, in real time, via cable and VHF connections. The data acquired have been employed for a wide variety of purposes including: monitoring the effects of climate change; characterization of pack ice properties relevant to offshore platform- and facility-design; studies of wave climates inside marginal ice zones; and provision of realtime assistance for navigation and ice management decision-making. Presently, special purpose ice profiling sonars are being incorporated into under-ice science-related missions using autonomous underwater vehicles (AUVs) and on manned submarines This presentation begins with a short outline of present profiling capabilities, identifying important characterizations of acoustic scattering by an ice undersurface and outlining the development of the high frequency sampling technique which is an essential element in providing the detail and accuracy required for most modern applications. Quantitative data are provided on key issues determining instrument performance and their implications for optimal use of similar instruments for identifying suitable "skylites" or patches of open water or thin ice suitable for bringing AUVs to the sea surface for recovery or to carry out operational tasks. Two fundamentally different identification approaches, based upon, respectively, echo amplitude and range measurements are discussed and related to typical AUV operating constraints and needs. It is concluded that neither approach will, in itself, meet user needs, necessitating future efforts toward development of a hybrid identification methodology in accord with suggested operating principles.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".