Surface Morphology and Subsurface Ice Content Relationships in Arcadia Planitia, Mars and the Canadian High Arctic
Bibliographic record
Abstract
As NASA and SpaceX prepare for future human missions to Mars as part of an In-situ Resource Utilization (ISRU) Space Act Agreement (SAA), we need more detailed characterization of ice at proposed landing sites to constrain ice accessibility, landing safety, and scientific value. Obtaining near-surface in situ water-ice can be used for rocket fuel and life support needs which would significantly reduce the mass needed for transport to and from Mars. Arcadia Planitia is the lowest-lying region in the northern hemisphere of Mars where abundant evidence exists for an ice-rich subsurface. Shallow Radar observations indicate a decameters-thick layer of water-ice (i.e., buried ice sheet) extends across much of Arcadia. The goal of my Ph.D. research is to characterize the ice-related features at Arcadia Planitia, a proposed future human mission landing site, in detail to assist in the identification of a safe landing site where water-ice is present and accessible for ISRU. By utilizing multiple orbital datasets (i.e., morphology, albedo, thermal infrared reflectance, thermal inertia, and subsurface radar reflections) and identification criteria for Viscous Flow Features (VFFs) on Mars, I mapped six glacial-related features in Arcadia. These units consist of conventional VFFs, such as Lobate Debris Aprons, and non-conventional VFFs. Three sinuous features in the flat-lying plains of Arcadia show surface morphologies and spectral properties indicating these are non-conventional VFFs of channelized ice that once flowed. I propose these sinuous features to be analogous to terrestrial ice streams. Brain terrain is proposed to represent a lag deposit formed atop thick glacial ice as a result of ice sublimation. However, we observe brain terrain to occur only within a narrow latitudinal band within the study site with minimal examples of brain terrain found on the six glacial-related features mapped. We utilize the Canadian High Arctic to investigate analogous brain terrain, that we have termed Vermicular Ridge Features (VRFs), to identify surface-subsurface relationships with ground-penetrating radar, photogrammetry, grain size analysis, and LiDAR. We interpret VRFs to be produced from the passive ablation of stagnant glacial ice. We interpret the lack of brain terrain on the six glacial-related features we mapped at Arcadia Planitia to represent regions where thick units of ice persist, have experienced less degradation than the surrounding terrain, and, therefore, where massive ice is shallower from the surface making our mapped regions areas where ice is more accessible for ISRU.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".