Creating Mineral "Frankenspectra" Using UV-VNIR-MIR Reflectance Data from Three Different Laboratories
Bibliographic record
Abstract
Reflectance spectroscopy is a useful method for identifying an unknown mineral, if its spectrum can be compared to a spectral library that includes quality spectra of the same mineral. However, a mineral sample measured in different labs will often result in varied spectra due to the way the sample is placed into the sample cup, the calibration of the data, the spectrometer hardware and light sources used, or other issues. Our team measured a suite of 28 mineral samples in 3 different labs to expose differences in the resulting spectra in order to modify the data acquisition strategy both to improve and converge on the best sample spectra. \n \nThe labs involved were the Planetary Spectroscopy Lab at the German Aerospace Center, the Centre for Terrestrial and Planetary Exploration at the University of Winnipeg, and the Planetary Geosciences Lab at the Planetary Science Institute. After the best data were acquired from each of the labs and plotted together, it was clear that no single lab/instrument offered the best overall spectrum; rather, each lab/instrument best presented a portion of the overall wavelength region studied. Therefore, a spectral library would benefit from one best-quality, most representative spectrum of a sample, constructed using the best wavelength pieces of the spectra acquired at various labs. \n \nWe are calling these blended/spliced mineral spectra “Frankenspectra”, i.e., spectra built in a laboratory and stitched together with human modification, akin to Dr. Frankenstein’s monster (but in a good way). \n \nWe will present the details of our samples, the laboratories, our ambient-temperature data from the 3 labs, as well as our derived Frankenspectra of the minerals. These data will be archived at the Planetary Data System Geosciences Node (GEO), including all the original individual spectra and the best-representative Frankenspectrum of each mineral. \n \nIn the near future we will be acquiring similar measurements and creating similar spectra with meteorite samples that currently are being prepared for us at NASA Johnson Space Center’s curatorial facility.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".