Spectral reflectance properties of zeolites and remote sensing implications
Bibliographic record
Abstract
The 0.3‐ to 26‐μm reflectance spectra of a suite of 28 zeolites were measured and analyzed to derive spectral‐compositional‐structural relationships. Below ∼7 μm, the spectra are largely dominated by absorption features associated with zeolitic water. At longer wavelengths, the spectra are dominated by absorption features associated with the aluminosilicate framework. The spectra exhibit a number of systematic variations which can be used for both structural and compositional determinations. These include: (1) distinguishing different structural groups on the basis of wavelength position variations associated with absorption features in the 8.5‐ to 26‐μm region that are related to differences in the structure of the aluminosilicate framework; (2) determining the major cation which is present (Ca, Na, K) and the associated electronic environment of the zeolitic water on the basis of how these cations hydrogen bond to the water molecules in the void spaces and consequently affect water‐related absorption band positions, particularly in the 1.4, 1.9, and 2.0‐ to 2.5‐μm regions; (3) determining the Al:(Al + Si) ratio and SCFM chemical index on the basis of absorption features in the 7‐ to 26‐μm region which are most sensitive to these compositional variations; and (4) identifying iron‐bearing zeolites on the basis of absorption features in the 0.35‐ to 0.9‐μm region. The wavelength position and number of H2O‐associated absorption bands are sensitive to factors such as the type of major cation, degree of hydrogen bonding, and size of the void space, all of which are somewhat interrelated.
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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 | 0.001 |
| 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 | 0.000 |
| 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".