Analytical database of Martian minerals (ADaMM): Project synopsis and Raman data overview
Bibliographic record
Abstract
Abstract The Mars2020/Perseverance and ExoMars/Rosalind Franklin rovers are both slated to return the first Raman spectra ever collected from another planetary surface, Mars. In order to optimize the rovers scientific outcome, the scientific community needs to be provided with tailored tools for data treatment and interpretation. Responding to this need, the purpose of the Analytical Database of Martian Minerals (ADaMM) project is to build an extended multianalytical database of mineral phases that have been detected on Mars or are expected to be found at the landing sites where the two rovers will operate. Besides the use of conventional spectrometers, the main objective of the ADaMM database is to provide access to data collected by means of laboratory prototypes simulating the analytical performances of the spectroscopic systems onboard the Mars 2020 and ExoMars rovers. Planned to be released to the public in 2022, ADaMM will also provide access to data treatment and visualization tools developed in the framework of the mentioned space exploration missions. As such, the present work seeks to provide an overview of the ADaMM online platform, spectral tools, and mineral collection. In addition to that, the manuscript describes the Raman spectrometers used to analyze the mineral collection and presents a representative example of the analytical performance ensured by the Raman prototypes assembled to simulate the Raman Laser Spectrometer (RLS) and SuperCam systems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".