Prospecting for Space Exploration
Bibliographic record
Abstract
Prospecting for terrestrial ore deposits relies on numerous methods ranging from large scale geophysical surveys to smaller scale geochemical sample analyses. Exploration entails physical methods, such as remote sensing and seismic or gravitational surveys to evaluate the surface and subsurface of the Earth to detect or infer the presence of valuable deposits. Geoscientists use 3D modeling to determine the geometry and placement of these deposits. A 3D model is a mathematical representation of a three dimensional region in order to evaluate the concentration, method of extraction and potential economic value of the deposit. In January 2010, the Northern Centre for Advanced Technology, Inc (NORCAT) demonstrated the ability to apply geotechnical criteria to acquired 3D data during a field test at approximately 9000 ft elevation on Mauna Kea in Hawaii. This activity was meant to mirror a lunar ISRU mission where robotic precursors are deployed and must survey the surroundings to allow ground operators to select a suitable location to begin construction of a landing site for suture lunar modules. It is necessary to ensure the excavation activity is only attempted in a location where the task is within the operational capability of the mobility platforms. The 3D model was created from surface data acquired by Neptec’s TriDAR and subsurface data acquired by Ground Penetrating Radar (GPR). The data was processed by Xiphos’ Hybrid Processing Card (HPS) for transmission over a limited bandwidth satellite link. RADARSAT-2 remote sensing satellite imaging was acquired prior to, during and following the field test. The imagery acquired provided useful data for base camp deployment, land use and site remediation. Satellite imagery can provide a comprehensive view of a broad area, and potentially enable detailed topographical, geological, geophysical, and environmental data acquisition, and is dependent upon the instruments onboard the satellite. The integration of such satellite imagery and data in NORCAT’s 3D model would present scientists the opportunity to evaluate numerous data types in one interactive tool. This paper describes the NORCAT 3D model and discusses the potential to integrate remote sensing satellite imagery into the model to enhance overall effectiveness for ISRU prospecting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.135 | 0.064 |
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".