Variations in the Near‐Infrared Spectral Properties of Ferrous Mineral Mixtures With Different Relative Abundances
Bibliographic record
Abstract
Abstract In near‐infrared spectral studies, the relationship between the 1‐μm absorption (Band I) center and the band area ratio (BAR, the area ratio of 2–1‐μm absorption features) is useful in compositional and mineralogical analyses of ferrous mineral‐bearing mixtures. Zhang and Cloutis (2020), https://doi.org/10.1029/2020ea001153 , investigated various lunar ferrous iron‐bearing rocks and minerals and found that pyroxene‐bearing materials rich in ilmenite (Ilm), plagioclase (Pl), or glass are offset from the lunar olivine‐clinopyroxene‐orthopyroxene (Ol‐Cpx‐Opx) mixing line in the plot of the BAR versus the Band I center. To analyze the variation trends of the spectral properties of these mixtures with different components, this study presents a systematic evaluation of laboratory spectra of terrestrial and synthetic ferrous iron‐bearing mineral mixtures based on published databases. In general, the mixing trends of the Pl‐pyroxene mixtures, the glass‐pyroxene mixtures, and the Ilm‐basalt mixtures are consistent with the findings of Zhang and Cloutis (2020), https://doi.org/10.1029/2020ea001153 . Moreover, this study also finds that the BAR of the Pl‐pyroxene mixtures varies nonlinearly with different relative abundances and that the BAR is generally not sensitive to Pl abundances below 60%. For glass‐pyroxene mixtures, the corresponding data points are usually appreciably offset from the Ol‐Cpx‐Opx mixing line at glass abundances above 20%. The BAR of the Ilm‐basalt mixtures increases with increasing Ilm content, mainly due to the weakening of 1‐μm absorption generally being greater than that of 2‐μm absorption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".