Tracing Carbonate Formation, Serpentinization, and Biological Materials With Micro‐/Meso‐Scale Infrared Imaging Spectroscopy in a Mars Analog System, Samail Ophiolite, Oman
Bibliographic record
Abstract
Visible-shortwave infrared (VSWIR) imaging spectrometers map composition remotely with spatial context, typically at many meters-scale from orbital and airborne data. Here, we evaluate VSWIR imaging spectroscopy capabilities at centimeters to sub-millimeter scale at the Samail Ophiolite, Oman, where mafic and ultramafic lithologies and their alteration products, including serpentine and carbonates, are exposed in a semi-arid environment, analogous to similar mineral associations observed from Mars orbit that will be explored by the Mars-2020 rover. At outcrop and hand specimen scales, VSWIR spectroscopy (a) identifies cross-cutting veins of calcite, dolomite, magnesite, serpentine, and chlorite that record pathways and time-order of multiple alteration events of changing fluid composition; (b) detects small-scale, partially altered remnant pyroxenes and localized epidote and prehnite that indicate protolith composition and temperatures and pressures of multiple generations of faulting and alteration, respectively; and (c) discriminates between spectrally similar carbonate and serpentine phases and carbonate solid solutions. In natural magnesite veins, minor amounts of ferrous iron can appear similar to olivine's strong 1-μm absorption, though no olivine is present. We also find that mineral identification for carbonate and serpentine in mixtures with each other is strongly scale- and texture-dependent; ∼40 area% dolomite in mm-scale veins at one serpentinite outcrop and ∼18 area% serpentine in a calcite-rich travertine outcrop are not discriminated until spatial scales of <∼1-2 cm/pixel. We found biological materials, for example bacterial mats versus vascular plants, are differentiated using wavelengths <1 μm while shortwave infrared wavelengths >1 μm are required to identify most organic materials and distinguish most mineral phases.
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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 | 0.000 |
| 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) | 0.001 | 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".