It's Time for Focused In Situ Studies of Planetary Surface-Atmosphere Interactions
Bibliographic record
Abstract
A critical gap in planetary observations has been in situ characterization of extra-terrestrial, present-day atmospheric and surface environments and activity. While some surface activity has been observed and some in situ meteorological measurements have been collected by auxiliary instruments on Mars, existing information is insufficient to conclusively characterize the natural processes via concurrent and high-resolution measurement of environmental drivers and activity. Thus, many atmospheric, aeolian, and other surface processes models - which are used to generate key constraints on science and exploration in many areas of planetary investigation-such as surface exposure/erosion estimates, landscape interpretation, and modeling dust storm development-remain untested under non-Earth conditions. Analogous terrestrial processes are often studied intensively via numerical modeling that integrates empirical results from laboratory and/or field studies of process-response interactions between the atmosphere and relevant surface landforms. Incorporation of such in situ measurements into model development has significantly advanced our understanding of atmosphere-surface interactions and related geomorphic processes on Earth, and is poised to do so on other planets. However, to date, such testing and refinement have not been possible in other planetary environments, partially because investigations of this sort require new technologies, mission architectures, and operations designs (e.g., different from large rovers focused on geochemical investigations) to fully address the key gaps in our understanding while keeping cost and risk low. Fortunately, technological developments in the areas of surface access, instrumentation, and onboard processing/memory now enable small spacecraft to accommodate meteorological and aeolian instrumentation that could collect the needed measurements to fill this critical gap while remaining within typical small spacecraft resource budgets. Furthermore, maturity of our understanding of the broader geologic and atmospheric context on Mars provides a ready framework for ingestion of discrete ground truth measurements into our understanding of the broader and multi-scale martian natural systems and processes. These advancements make addressing key science questions with novel mission concepts feasible, promising results that would significantly advance our understanding of extraterrestrial surface-atmosphere interactions. This summary follows from a community-generated white paper for the ongoing Planetary Science/Astrobiology Decadal Survey, small spacecraft concept development at JPL, and numerous JPL and community discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".