Development of an Integrated Vision System (IVS) for Characterization of the Lunar Surface
Bibliographic record
Abstract
The integrated vision system (IVS) is a prototype stand-off instrument that would be mounted on a rover mast for planetary exploration. The newly envisioned IVS instrument concept is currently being redesigned for rapid reconnaissance of the lunar surface through the integration of a multispectral imager and a multi-wavelength LIDAR. The wide band multispectral camera uses two types of image sensors to cover the wide wavelength from 250 to 2,500 nm. This is a novel development as all previous and current context imagers on landers or rovers have only had the capability to acquire images up to 1,000 nm. We demonstrate the clear links between the potential capabilities of the IVS to strategic knowledge gaps related to lunar resource exploration. We describe the primary scientific goals and objectives of the instrument, and investigation measurement objectives that can be traced to specific instrument requirements. We demonstrate how the IVS would be able to rapidly differentiate between different types of lunar materials through the use of band ratios and spectral parameters on orbital datasets. Through these analyses, we identify a nominal list of filter wavelengths for the IVS that would be most suitable for lunar exploration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".