Physical characteristics of Martian south polar ices determined by spatial- and intimate-mixture modeling
Bibliographic record
Abstract
Twenty-one spectral types for the perennial Martian south polar ices were determined from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) data using k-means clustering. These data are higher spatial resolution than any previous measurement of these ices. We present both linear and radiative transfer models of those CRISM spectral types to determine the relative abundance and effective grain size of the three components that make up these surfaces: carbon dioxide ice, water ice and non-ice material. Most cluster means are well represented by a linear combination of five spectra, two types of CO 2 ice, two types of water ice and a non-ice surface. To first order, each CRISM pixel is comprised of sub-pixel spatial mixtures of these five. We then use radiative transfer models to determine the best fit effective grain size and relative abundance of the five components. We use a relatively featureless area of the polar layered deposits for the non-ice component. Our models are consistent with prior modeling work and show very large grain sizes (mm to cm) in the residual CO 2 with very small amounts of water (<0.04 wt%). Water dominated terrains have a wide range of grain sizes but are uniformly ~80 % water ice. We identify a unique spectral type (C1) that does not have an equivalent in prior studies and may represent CO 2 ice deposited when the atmosphere contained less water vapor following the dust storm of MY28. Plain language summary Ices at the south pole of Mars show absorption features associated with water ice, carbon dioxide ice and non-ice material. Prior work identified twenty-one spectral types that range in appearance from only carbon dioxide ice (CO 2 ) to only water ice (H 2 O) with many mixtures in between these two. For the first time we model these mixed spectra using both linear least squares statistical approaches and more sophisticated models that account for the interaction of light with individual material grains where different grains are closely packed together. Most mixed spectra are well matched by the simple linear least squares fit of five different components. We then model those five components using the close-packing model. We find for the CO 2 only ices the grains are very large and have minute amounts of water. For water ice dominated spectra they consistently are ~80 % water 20 % non-ice material. One spectral type is unique and may reflect special conditions that occurred during one year when Mars experienced a large global dust storm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".