Raman Laser Spectrometer (RLS) calibration target design to allow onboard combined science between the RLS and MicrOmega instruments on the ExoMars rover
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The ExoMars rover, scheduled to be launched in 2020, will be equipped with a novel and diverse payload. It will also include a drill to collect subsurface samples (from 0‐ to 2‐m depth) and deliver them to the rover analytical laboratory, where it will be possible to perform combined science between instruments. For the first time, the exact same sample target areas will be investigated using complementary analytical methods—infrared spectrometry, Raman spectrometry, and laser desorption mass spectrometry—to establish mineralogical and organic chemistry composition. Fundamental for implementing this cooperative science strategy is the Raman Laser Spectrometer (RLS) calibration target (CT). The RLS CT features a polyethylene terephthalate disk used for RLS calibration and verification of the instrument during the mission. In addition, special patterns have been recorded on the RLS CT disk that the other instruments can detect and employ to determine their relative position. In this manner, the RLS CT ensures the spatial correlation between the three analytical laboratory instruments: MicrOmega, RLS, and MOMA. The RLS CT has been subjected to a series of tests to qualify it for space utilization and to characterize its behavior during the mission. The results from the joint work performed by the RLS and MicrOmega instrument teams confirm the feasibility of the “combined science” approach envisioned for ExoMars rover operations, whose science return is optimized when complementing the RLS and MicrOmega joint analysis with the autonomous RLS operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it