Development of an underwater fiber-optic lidar for the characterization of sea water and ice properties
Bibliographic record
Abstract
DRDC Valcartier has developed a unique underwater lidar for the measurement of different sea water and ice properties. The lidar head is designed for underwater operation and consists of four telescopes that are connected to the detection and emission unit via five 42 m fused silica optical fibers. Three telescopes are used for data collection, while the fourth is used for laser emission. The laser source and the detection unit are located on a surface vessel. The laser beam is injected into a 100 μm diameter optical fiber. The collimation of the laser beam is done in the lidar head via a lens with 25 mm diameter and 45 mm focal length; the laser beam is linearly polarized using a polarization beamsplitter. A 50 mm receiving telescope co-aligned with the laser beam is used for linear depolarization measurements. A second 50 mm telescope is used to collect off-axis scattered light while a third 50 mm telescope is used to collect inelastic scattered radiation (Raman and induced fluorescence signal). The laser source and detection units are mounted on a small optical table for easy access/modification. Various laser sources and lidar detection techniques (Q-switched pulses or frequency modulated) could be easily implemented. The lidar head can be deployed underwater or mounted on an airborne platform. In this work, the lidar system will be described in detail and preliminary results obtained with a Q-Switch, 532 nm, 1 ns pulse laser source will be presented and compared with the anticipated performance for different water bodies.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".