Neptec 3D Laser Camera System: from space mission STS-105 to terrestrial applications
Bibliographic record
Abstract
Neptec Design Group has developed the Laser Camera System (LCS), a 3D autosynchronized laser scanner based on a principle originating from the National Research Council of Canada. In imaging mode, the LCS raster scans objects and captures reflections from their surface features. In centroid acquisition mode, the LCS determines the position of discrete target points on an object. Neptec Design Group first developed the LCS for space applications. In August 2001, theLCS flew successfully onboard space shuttle Discovery during mission STS-105 to theInternational Space Station (ISS). During the mission, the LCS took four high-resolution(1024x1024 voxels) images of elements of the ISS during orbital day and night, demonstrating its immunity to dynamic lighting conditions. The LCS also tracked targets affixed to the Multi-Purpose Logistics Module (MPLM) while in motion, more than 10 m away. Comparison with Space Station Remote Manipulator System (SSRMS) data confirmed that the LCS tracked these targets with millimetre precision. Following the mission, the LCS has been demonstrated in a variety of terrestrial commercial applications. Two examples from the earth sciences include imaging rock faces in an underground mine to delineate joints to design adequate tunnel support, and acquiring detailed images of sandstone masonry walls. The high-fidelity spatial information and the intensity data captured by the LCS concurrently make it a valuable tool for the classification of geomaterials based on reflectivity and texture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".