Testing materials to mitigate terrestrial organic contamination of meteorites: Implications for collection, curation, and handling of astromaterials
Bibliographic record
Abstract
Abstract Organic matter in astromaterials can provide important information for understanding the chemistry of our solar system and the prebiotic conditions of the early Earth. However, once astromaterials reach the Earth's surface, they can be readily contaminated through contact with the Earth's surface as well as during processing and curation. Here, we investigate how typical handling and curation materials interact with meteorite specimens by documenting hydrophobic organic compound contamination in the laboratory environment and on materials that might be used for their collection and storage. We use gas chromatography–mass spectrometry analysis of soluble organic compounds in dichloromethane extracts of these materials to gain insights into what materials and methods are best for the collection and curation of astromaterials. Our results have implications for how extraterrestrial samples—especially those containing significant intrinsic organic matter—are handled and curated to preserve them in their most pristine states. Following recommendations of other researchers in the area of returned sample curation, we advocate for a thorough investigation into the materials used in handling and curation of meteorites to create a contamination baseline to inform soluble organic analyses on astromaterials and enable the discrimination of terrestrial and extraterrestrial compounds.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".