Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data
Bibliographic record
Abstract
Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data as described in Lemelin et al. (2022). This folder includes different GeoTIFF files described and shown in Lemelin et al. (2022). The files are projected in Polar Stereographic Projection, at a spatial resolution of 1000 m/pixel. A version of each of the following file is given for the north and south polar region (50-90 N/S). - Gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm - Data counts used in the gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm - Spectral Profiler FeO mosaics - Spectral Profiler OMAT mosaics - Spectral Profiler plagioclase mosaics - Spectral Profiler olivine mosaics - Spectral Profiler low-calcium pyroxene mosaics - Spectral Profiler high-calcium pyroxene mosaics - Nanophase iron mosaics - Correlation coefficient mosaics
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.033 |
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".