Effect of halite coatings on thermal infrared spectra
Bibliographic record
Abstract
Abstract Characterizing the occurrence and distribution of soluble salts on planetary surfaces allows us to model or monitor aqueous and geochemical conditions. Thermal infrared (TIR) remote sensing is useful for identifying some types of salt deposits; however, mineral abundance determinations are based on the interaction of infrared with only the top few hundred micrometers of the surface. Thus, distinguishing massive deposits from coatings presents a challenge to TIR remote sensing. To better understand the TIR properties of spectrally transmissive halite coatings, we investigated the effects of coating thickness and texture on the TIR reflectance spectra of halite‐coated glasses. We analyzed two coating textures with variable thickness: (1) continuous and (2) discontinuous particulate coatings. As halite coating thickness increases, the intensity of substrate absorption bands decreases nonlinearly. The substrate is not detected with halite coatings >150 µm thick. Therefore, when coatings are present, it is not possible to apply TIR models of mineral abundances using linear deconvolution algorithms. We show that continuous and coarse particulate halite coatings increase the reflectance minimum (emissivity maximum), making them potentially detectable on planetary surfaces by the methods of Osterloo et al. (2008). However, the reflectance minimum does not change if coatings have low areal coverage (<50%) or are composed of fine particles (<5 µm). Thus, halite that forms as efflorescent coatings or has been redistributed as fine dust is not detected using TIR spectroscopy. Our results explain why Cl salts are not detected with TIR spectroscopy at many locations on Mars despite high Cl contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".