In harbor underwater threat detection/identification using active imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present results from trials of the LUCIE 2 (Laser Underwater Camera Image Enhancer) conducted in Halifax Harbor, Nova Scotia, Canada and Esquimalt Harbor, Victoria, British Columbia, Canada. LUCIE 2 is a new compact laser range gated camera (10 inches in diameter, 24 inches in length, and neutrally buoyant in water) originally designed to improve search and recovery operations under eye safe restrictions. The flexibility and eye safety of this second generation LUCIE makes it a tool for improved hull searches and force protection operations when divers are in the water attempting to identify bottom lying objects. The camera is equipped with a full image geo-positioning system. To cover various environmental and targets size conditions, the gate-delay, gate width, polarization and viewing and illuminating angles can be varied as well. We present an analysis on the performance of the system in various water conditions using several target types and a comparison with diver and camera identification. Coincident in-situ optical properties of absorption and scattering were taken to help resolve the environmental information contained in the LUCIE image. Several new capabilities are currently being designed and tested, among them a differential polarization imaging system, a stabilized line of sight system with step-stare capability for high resolution mosaic area coverage, a precision dimensioning system and a diver guided and operated version.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Open science | 0.001 | 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 it