Mid-infrared emission spectroscopy and visible/near-infrared reflectance spectroscopy of Fe-sulfate minerals
Bibliographic record
Abstract
Sulfate minerals are important indicators for aqueous geochemical environments. The geology and mineralogy of Mars have been studied through the use of various remote-sensing techniques, including thermal (mid-infrared) emission and visible/near-infrared reflectance spectroscopies. Spectral analyses of spacecraft data (from orbital and landed missions) using these techniques have indicated the presence of sulfate minerals on Mars, including Fe-rich sulfates on the iron-rich planet. Each individual Fe-sulfate mineral can be used to constrain bulk chemistry and lends more information about the specific formational environment [e.g., Fe 2+ sulfates are typically more water soluble than Fe 3+ sulfates and their presence would imply a water-limited (and lower Eh) environment; Fe 3+ sulfates form over a range of hydration levels and indicate further oxidation (biological or abiological) and increased acidification]. To enable better interpretation of past and future terrestrial or planetary data sets, with respect to the Fe-sulfates, we present a comprehensive collection of mid-infrared thermal emission (2000 to 220 cm -1 ; 5-45 μm) and visible/near-infrared (0.35-5 μm) spectra of 21 different ferrous- and ferric-iron sulfate minerals. Mid-infrared vibrational modes (for SO 4 , OH, H 2 O) are assigned to each thermal emissivity spectrum, and the electronic excitation and transfer bands and vibrational OH, H 2 O, and SO 4 overtone and combination bands are assigned to the visible/near-infrared reflectance spectra. Presentation and characterization of these Fe-sulfate thermal emission and visible/near-infrared reflectance spectra will enable the specific chemical environments to be determined when individual Fe-sulfate minerals are identified.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".