Sea-ice detection for autonomous underwater vehicles and oceanographic lagrangian platforms by continuous-wave laser polarimetry
Bibliographic record
Abstract
The use of Lagrangian platforms and of Autonomous Underwater Vehicles (AUVs) in oceanography has increased rapidly over the last decade along with the development of improved biological and chemical sensors. These vehicles provide new spatial and temporal scales for observational studies of the ocean. They offer a broad range of deployment and recovery capabilities that reduce the need of large research vessels. This is especially true for ice-covered Arctic ocean where surface navigation is only possible during the summer period. Moreover, safe underwater navigation in icy waters requires the capability of detecting sea ice on the surface (ice sheets). AUVs navigating in such conditions risk collisions, RF communication shadowing, and being trapped by ice keels. In this paper, an underwater sea-ice detection apparatus is described. The source is a polarized continuous wave (CW) diode-pumped solid-state laser (DPSS) module operating at 532 nm. The detector is composed of a polarizing beam splitter, which separates light of S and P polarization states and two photodetectors, one for each polarized component. Since sea-ice is a strong depolarizer, the ratio P/S is an indicator of the presence or absence of sea-ice. The system is capable of detecting sea-ice at a distance of 12m. This apparatus is designed to be used by free drifting profiling floats (e.g., Argo floats), buoyancy driven vehicles (e.g., sea gliders) and propeller-driven robots (e.g., Hugin class AUV).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".