Ilmenite mapping of the lunar regolith over Mare Australe and Mare Ingenii regions: An optimized multisource approach based on Hapke radiative transfer theory
Bibliographic record
Abstract
Abstract We model lunar ilmenite abundances over Mare Australe and Mare Ingenii regions using a new approach; we integrate Lunar Reconnaissance Orbiter Wide Angle Camera (WAC) and Clementine UV‐visible/near‐infrared (UVVIS/NIR) data to obtain a 14‐band mosaic (320‐2000 nm). We use Hapke's radiative transfer equations to compute spectra for various mixtures of orthopyroxene, clinopyroxene, plagioclase, olivine, and ilmenite, with varying grain size, chemistry, and degree of maturity, and find the closest match between the modeled spectra and the spectra of the less mature pixels (optical maturity ≥ 0.2) in the 14‐band mosaic. We calculate a “maximum stoichiometrically possible ilmenite content”, using Clementine‐derived TiO2 abundances and the amount of TiO2 in stoichiometric ilmenite, and use it as a constraint in our model. We validate our methodology with lunar soil spectra of known composition. Our results show that the integrated WAC‐UVVIS/NIR data and the UVVIS/NIR data overestimate ilmenite abundances by 8.80 wt % and 7.97 wt %, respectively, when a fixed maximum of 20 wt % ilmenite is used. When the maximum stoichiometrically possible ilmenite content is used as a constraint, the integrated WAC‐UVVIS/NIR data give slightly more accurate ilmenite abundance estimation (±2.87 wt %) than when using only UVVIS/NIR data (± 3.04 wt %). We find ilmenite concentrations of 0−11 wt % in Mare Australe and 0−6 wt % in Mare Ingenii region. Ilmenite abundances between 4 and 7 wt % are exposed in Mare Australe, whereas ilmenite abundances between 7 and 11 wt % are found on the walls of 0.6‐11.8 km diameter craters within Mare Australe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".