An Assessment of macro-scale<i>in situ</i>Raman and ultraviolet-induced fluorescence spectroscopy for rapid characterization of frozen peat and ground ice
Bibliographic record
Abstract
Abstract The search for life on other planets is an active area of research. Many of the likeliest planetary bodies, such as Europa, Enceladus, and Mars are characterized by cold surface environments and ice-rich terrains. Both Raman and ultraviolet-induced fluorescence (UIF) spectroscopies have been proposed as promising tools for the detection of various kinds of bioindicators in these environments. We examined whether macro-scale Raman and UIF spectroscopy could be applied to the analysis of unprocessed terrestrial frozen peat and clear ground ice samples for detection of bioindicators. It was found that this approach did not provide unambiguous detection of bioindicators, likely for a number of reasons, particularly due to strong broadband induced fluorescence. Other contributing factors may include degradation of organic matter in frozen peat to the point that compound-specific emitted fluorescence or Raman peaks were not resolvable. Our study does not downgrade the utility of either UIF or Raman spectroscopy for astrobiological investigations (which has been demonstrated in previous studies), but does suggest that the choice of instrumentation, operational conditions and sample preparation are important factors in ensuring the success of these techniques.
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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) | 0.000 | 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 | 0.000 |
| 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".