Mineral maps for Dawes crater using Multiband Imager (MI_MAP_02) reflectance data
Bibliographic record
Abstract
These mineral maps are derived using Multiband Imager (MI_MAP_02) reflectance data and Hapke radiative transfer modeling. These maps present the abundance of the four major lunar minerals (plagioclase, olivine, low-Ca pyroxene, high-Ca pyroxene) at a spatial resolution of ~62 m/pixel. Details about the methods can be found in Lemelin et al. (2015) and Lemelin et al. (2019). These maps suggest that at least 3 pixels, corresponding to approximately 11,500 m2, at the bottom of the Dawes crater match the mineral composition of the Apollo 17 norites studied by Cernok et al. (2021): 6-7 wt. % olivine, 47-52 wt. % orthopyroxene, 6-7 wt. % clinopyroxene and 35-41 wt. % plagioclase. Cernok et al. (2021) Sample-based evidence for an ancient (~4.2 Gyr) formation of the Serenitatis Basin on the Moon, Communications Earth & Environment (In press). Lemelin et al. (2015) Lunar central peak mineralogy and iron content using the Kaguya Multiband Imager: Reassessment of the compositional structure of the lunar crust, JGR - Planets, 120(5), 869-887. Lemelin et al. (2019) The compositions of the lunar crust and upper mantle: Spectral analysis of the inner rings of lunar impact basins, Planetary and Space Science, 165, 230-243.
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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 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.011 | 0.004 |
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".