Contamination Knowledge Strategy for the Mars 2020 Sample-Collecting Rover
Bibliographic record
Abstract
The Mars 2020 rover will collect carefully selected samples of rock and regolith as it explores a potentially habitable ancient environment on Mars. Using the drill, rock cores and regolith will be collected directly into ultraclean sample tubes that are hermetically sealed and, later, deposited on the surface of Mars for potential return to Earth by a subsequent mission. Thorough characterization of any contamination of the samples at the time of their analysis will be essential for achieving the objectives of Mars returned sample science (RSS). We refer to this characterization as contamination knowledge (CK), which is distinct from contamination control (CC). CC is the set of activities that limits the input of contaminating species into a sample, and is specified by requirement thresholds. CK consists of identifying and characterizing both potential and realized contamination to better inform scientific investigations of the returned samples. Based on lessons learned by other sample return missions with contamination-sensitive scientific objectives, CC needs to be owned by engineering, but CK needs to be owned by science. Contamination present at the time of sample analysis will reflect the sum of contributions from all contamination vectors up to that point in time. For this reason, understanding the integrated history of contamination may be crucial for deciphering potentially confusing contaminant-sensitive observations. Thus, CK collected during the Mars sample return (MSR) campaign must cover the time period from the initiation of hardware construction through analysis of returned samples in labs on Earth. Because of the disciplinary breadth of the scientific objectives of MSR, CK must include a broad spectrum of contaminants covering inorganic (i.e., major, minor, and trace elements), organic, and biological molecules and materials.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".