Field Testing of an Integrated Surface/Subsurface Modeling Technique for Planetary Exploration
Bibliographic record
Abstract
While there has been much interest in developing ground-penetrating radar (GPR) technology for rover-based planetary exploration, relatively little work has been done on the data collection process. Starting from the manual method, we fully automate GPR data collection using only sensors typically found on a rover. Further, we produce two novel data products: (1) a three-dimensional, photorealistic surface model coupled with a ribbon of GPR data, and (2) a two-dimensional, topography-corrected GPR radargram with the surface topography plotted above. Each result is derived from only the onboard sensors of the rover, as would be required in a planetary exploration setting. These techniques were tested using data collected in a Mars analogue environment on Devon Island in the Canadian High Arctic. GPR transects were gathered over polygonal patterned ground similar to that seen on Mars by the Phoenix Lander. Using the techniques developed here, scientists may remotely explore the interaction of the surface topography and subsurface structure as if they were on site.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".