USING HYPERSPECTRAL UAS TO MAP HYDRATED COMPOUNDS IN MARTIAN TERRESTRIAL ANALOG SYSTEMS
Bibliographic record
Abstract
Hypersaline lakes and playas in arid environments on Earth can serve as analogs for paleolake environments of interest on Mars because the hydrated compounds within these systems indicate the presence of ancient hydrologic networks and display strong relationships with microbial life. Traditional orbital observations map these compounds at spatial resolutions ranging from tens to hundreds of meters per pixel. This study uses in-situ measurements, Uncrewed Aerial Systems (UAS) hyperspectral mapping (10 cm), and EnMAP satellite hyperspectral mapping (30 m) to map heterogeneous mineralogical assemblages in hypersaline lakes in Southcentral British Columbia, Canada (BC). We tested the ability of high-resolution UAS hyperspectral mapping to identify hydrated compounds within these hypersaline lakes. The hyperspectral UAS classifications had a 92% agreement with ground spectrometer (ASD) classifications and 100% agreement with X-ray diffractometer (XRD) mineral identifications from in-situ samples. UAS and ASD percent confidence and XRD target compound concentration values also agreed well, indicating that the UAS can map the target compounds' relative abundances across the lakes’ surface. ENMAP hyperspectral imagery was able to differentiate the hypersaline lakes from the surrounding area but was not able to capture individual compounds, relative abundance, or spatial variability within the lakes. The distribution of hydrated compounds was strongly related to physical features within the lakes. When the features were mapped using UAS lidar surveys, there was a strong correlation between the target compound distributions and the lakes’ physical features, further linking the chemistry to the geomorphology of these systems. The multiscale mapping procedure suggests that UAS hyperspectral mapping is a valuable method for characterizing the surface mineralogy of astrobiologically relevant environments because spatial and spectral resolutions beyond the current capabilities of orbital imagery can be achieved
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".