Planetary Geologic Maps: Essential Tools for Scientific Inquiry and Space Exploration
Bibliographic record
Abstract
Abstract Planetary geologic maps are crucial tools for understanding the geological features and processes of solid bodies in the Solar System. Over the past six decades, best practices in planetary geologic mapping have emphasized clear and objective observation, geological interpretation, multi‐sensor fusion, and iterative revision of maps based on new data. We summarize here four ways in which maps serve as indispensable instruments for scientific investigation, from enhancing observations to interrogating surface processes. With respect to space exploration, we underscore the role of planetary geologic maps as tools to link testable, hypothesis‐driven science to exploration goals and provide actionable information for hazard identification, resource evaluation, sample collection, and potential infrastructure development. To further advance the field of planetary geologic mapping, international collaboration is essential. This includes sharing data and maps through FAIR (findable, accessible, interoperable, and reusable) platforms, establishing standardized mapping practices, promoting diverse nomenclature, and fostering continued cooperation in space 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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".