Maximizing Scientific Exploitation of Raman Spectroscopy With A.C.M.E. (Atmospheric Chamber for Measurements in Environment)
Bibliographic record
Abstract
ABSTRACT The Atmospheric Chamber for Measurements in Environment (A.C.M.E.) provides a versatile and highly controlled environment for simulating planetary conditions, supporting the testing and calibration of instruments for planetary exploration. In this study, we utilized A.C.M.E. to evaluate the performance of a novel hollow‐core fiber (HCF) Raman gas sensor prototype developed by the ERICA research team. By integrating the HCF sensor with a dedicated spectrometer, we confirmed that Raman spectrometers, such as the Raman Laser Spectrometer (RLS), could be used for atmospheric gas analysis in future planetary missions, expanding their applications beyond mineralogical studies. By using the A.C.M.E. chamber to produce representative gas mixtures, this work analytically demonstrated that, once optimized, the HCF sensor prototype could be potentially used to investigate the atmosphere of both Mars and Venus in future planetary missions. These findings underscore the critical role of atmospheric chambers like A.C.M.E. in advancing technologies for future planetary exploration missions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".