Life Detection and Microbial Biomarker Profiling with Signs of Life Detector-Life Detector Chip During a Mars Drilling Simulation Campaign in the Hyperarid Core of the Atacama Desert
Bibliographic record
Abstract
The low organic matter content in the hyperarid core of the Atacama Desert, together with abrupt temperature shifts and high ultraviolet radiation at its surface, makes this region one of the best terrestrial analogs of Mars and one of the best scenarios for testing instrumentation devoted to in situ planetary exploration. We have operated remotely and autonomously the SOLID-LDChip (Signs of Life Detector-Life Detector Chip), an antibody microarray-based sensor instrument, as part of a rover payload during the 2019 NASA Atacama Rover Astrobiology Drilling Studies (ARADS) Mars drilling simulation campaign. A robotic arm collected drilled cuttings down to 80 cm depth and loaded SOLID to process and assay them with LDChip for searching for molecular biomarkers. A remote science team received and analyzed telemetry data and LDChip results. The data revealed the presence of microbial markers from Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria, Firmicutes, and Cyanobacteria to be relatively more abundant in the middle layer (40–50 cm). In addition, the detection of several proteins from nitrogen metabolism indicates a pivotal role in the system. These findings were corroborated and complemented on “returned samples” to the lab by a comprehensive analysis that included DNA sequencing, metaproteomics, and a metabolic reconstruction of the sampled area. Altogether, the results describe a relatively complex microbial community with members capable of nitrogen fixation and denitrification, sulfur oxidation and reduction, or triggering oxidative stress responses, among other traits. This remote operation demonstrated the high maturity of SOLID-LDChip as a powerful tool for remote in situ life detection for future missions in the Solar System.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".