Eye-Safe Digital 3-D Sensing for Space Applications
Bibliographic record
Abstract
. This paper focuses on the characteristics and performance of an eye-safe laser range scanner (LARS) with short- and medium-range 3-D sensing capabilities for space applications. This versatile LARS is a precision measurement tool that will complement the current Canadian Space Vision System. The major advantages of the LARS over conventional video-based imaging are its ability to operate with sunlight shining directly into the scanner and its immunity to spurious reflections and shadows, which occur frequently in space. Because the LARS is equipped with two high-speed galvanometers to steer the laser beam, any spatial location within the field of view of the camera can be addressed. This versatility enables the LARS to operate in two basic scan pattern modes: (1) variable-scan-resolution mode and (2) raster-scan mode. In the variable-resolution mode, the LARS can search and track targets and geometrical features on objects located within a field of view of 30 by 30 deg and with corre...
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".