Challenges in the Search for Perchlorate and Other Hydrated Minerals With 2.1‐μm Absorptions on Mars
Bibliographic record
Abstract
A previously unidentified artifact has been found in Compact Reconnaissance Imaging Spectrometer for Mars targeted I/F data. It exists in a small fraction (<0.05%) of pixels within 90% of images investigated and occurs in regions of high spectral/spatial variance. This artifact mimics real mineral absorptions in width and depth and occurs most often at 1.9 and 2.1 μm, thus interfering in the search for some mineral phases, including alunite, kieserite, serpentine, and perchlorate. A filtering step in the data processing pipeline, between radiance and I/F versions of the data, convolves narrow artifacts ("spikes") with real atmospheric absorptions in these wavelength regions to create spurious absorption-like features. The majority of previous orbital detections of alunite, kieserite, and serpentine we investigated can be confirmed using radiance and raw data, but few to none of the perchlorate detections reported in published literature remain robust over the 1.0- to 2.65-μm wavelength range. PLAIN LANGUAGE SUMMARY: Many minerals can be identified with remote sensing data by their characteristic absorptions in visible-shortwave infrared data. This type of data has allowed geological interpretation of much of Mars' surface, using satellite-based observation. We have discovered an issue with the Compact Reconnaissance Imaging Spectrometer for Mars instrument's data processing pipeline. In ~ <0.05% of pixels in almost all images, noise in the data is smoothed in such a way that it mimics real mineral absorptions, falsely making it look as though certain minerals are present on Mars' surface. The vast majority of previously identified minerals are still confirmed after accounting for the artifact, but some to all perchlorate detections and a few serpentine detections were not confirmed, suggesting that the artifact created false detections. This means concentrated regions of perchlorate may not occur on Mars and so may not be available to generate possibly habitable salty liquid water at very cold temperatures.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".