Spectral properties of Martian and other planetary glasses and their detection in remotely sensed data
Bibliographic record
Abstract
Abstract Thirty silicate glasses were synthesized as realistic analogs to those expected to exist on Mars, the Moon, and Mercury. Samples were measured using visible/near‐infrared and Mössbauer spectroscopy to determine the effects of varying bulk chemistry, oxygen fugacity, and temperature on spectral properties. For Martian glasses, the fO2 during fusion strongly affects absorption band intensities in the spectra, while bulk chemistry has noticeable secondary effects on absorption band positions. Titanium and iron content drive spectral changes in lunar glasses, where Fe3+ is effectively absent. Iron‐free Mercury analog glasses have much higher albedos than all other samples, and their spectral shape is a close match to some pyroclastic deposits on Mercury. Synthetic glass spectra were used as inputs into a spectral unmixing model applied to remote orbital datasets to test for the presence of glass. The model is validated against physical laboratory mixture spectra, as well as previous detections of glass‐rich pyroclastic deposits on the Moon. Remote data were then used from suspected impact deposits and possible pyroclastic deposits on Mars as a new application of the model: the results reveal spatially coherent glass‐rich material, and the strong spectral match of the synthetic glasses to these remotely sensed data gives new insights into the presence and character of glasses on the Martian surface. The large library of glass spectra generated here, acquired from consistently synthesized and measured samples, can serve as a resource for further studies of volcanic and impact processes on planetary bodies.
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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 | 0.001 |
| 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) | 0.001 | 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".