Documenting microbial populations within the University of Alberta Meteorite Curation Facility using <scp>16S rRNA</scp> gene sequencing: Implications for the curation of astromaterials
Bibliographic record
Abstract
Abstract Meteorites are easily contaminated at the Earth's surface by microbial activity. Here, DNA extracts from two meteorite specimens and samples from curation laboratory surfaces are analyzed with amplicon sequencing, to understand microbial communities that contaminate meteorites and that may be resident in curation facilities. In addition, two different DNA extraction kits, the PowerSoil DNA Isolation Kit and the QIAamp UCP Pathogen Mini Kit, are utilized to determine if certain kits are more favorable for low biomass studies of meteorites. We find that, regardless of the type of kit used, the majority of microbial taxa that dominate meteorite and meteorite curation environments include those that are prevalent in soils or in the human microbiome. Our results have implications for advanced curation methods to protect the intrinsic properties of meteorites, such as extraterrestrial organics and minerals, from microbes. Preserving meteorites in pristine states and understanding the complex relationship between meteorites and terrestrial microbes can inform our search for the origin of life or life elsewhere in the universe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".