Characterization of the OPAL LiDAR under controlled obscurant conditions
Bibliographic record
Abstract
Neptec Technologies’ OPAL-120 3D LiDAR is optimized for obscurant penetration. The OPAL-120 uses a scanning mechanism based on the Risley prism pair. The scan patterns are created by rotating two prisms under independent motor control. The geometry and material properties of the prisms define the conical field-of-view of the sensor, which can be built to between 60 to 120 degrees. The OPAL-120 was recently evaluated using a controlled obscurant chamber capable of generating clouds of obscurants over a depth of 22m. Obscurants used in this investigation include: Arizona road dust, water fog, and fog-oil. The obscurant cloud optical densities were monitored with a transmissometer. Optical depths values ranged from an upper value of 6 and progressively decreased to 0. Targets were positioned at the back of the obscurant chamber at a distance of 60m from the LiDAR. The targets are made of a foreground array of equally spaced painted wood stripes in front of a solid background. Reflectivity contrasts were achieved with foreground/background combinations of white/white, white/black and black/white. Data analysis will be presented on the effect of optical densities on range and cross-range resolution, and accuracy. The analysis includes the combinations of all obscurant types and target reflectivity contrasts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".