UV laser‐induced fluorescence spectroscopy as a non‐destructive technique for mineral and organic detection in carbonaceous chondrites
Bibliographic record
Abstract
Abstract Here, we discuss the merits of non‐destructive UV laser‐induced fluorescence spectroscopy (LIF) as a flight or laboratory instrument to analyze organic and mineral material in samples on or returned from carbon‐rich asteroids such as (101955) Bennu by NASA’s OSIRIS‐REx mission. LIF is a unique instrument that is non‐destructive while acquiring data, and allows for no sample preparation, crushing, or cutting. This method provides spectral data indicative of specific minerals and organics in less time than Raman spectroscopy, and can be set up to produce 2‐D raster images of areas of interest. Furthermore, if an LIF system is set up with a gated CCD camera, time‐resolved fluorescence spectroscopy can be performed, providing a unique identification tool for organic and mineral contents using fluorescence decay over several nanoseconds. This technique was used to analyze millimeter‐sized chondrules and calcium‐aluminum‐rich inclusions on four carbonaceous chondrite samples provided by the Royal Ontario Museum: Murchison (CM2), Allende (CV3), NWA 11554 (CV3), and NWA 12796 (CK3). The LIF 2‐D maps, point spectra, and time‐resolved fluorescence data and mineral identifications using LIF were compared to that of well‐known techniques such as Raman spectroscopy and SEM/EDS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".