Sample Science Traceability Matrix for <i>Perseverance</i> ’s Mars Sample Return Collection
Bibliographic record
Abstract
The Mars Sample Return (MSR) Campaign aims to retrieve a set of carefully selected and documented samples collected by NASA’s Perseverance rover in and around Jezero Crater on Mars and deliver this set to Earth for comprehensive laboratory analyses. To emphasize the immense scientific return of this unique collection, this work presents a Sample Science Traceability Matrix (SSTM), a systematic framework that aligns each sample with the MSR campaign’s defined science objectives, subobjectives, and critical research questions. The SSTM explicitly connects prioritized goals—including geologic history, astrobiology, planetary evolution, and human exploration science—to each of the individual samples gathered in and around Jezero Crater on Mars. This matrix offers a structured, quantitative method to assess each sample’s capacity to address key scientific questions, while highlighting synergies across the sample suite and showcasing the overall value of the collection. The SSTM provides a valuable tool for guiding future sample analyses and identifying the most impactful samples that could be collected in the future to complete the set collected by the Mars 2020 mission. It also supports the next phase of Mars sample science and informs strategies for future Mars exploration missions. Key Words: Mars Sample Return— Perseverance —Jezero Crater—Laboratory—Sample collection—Science goals. Astrobiology 25, 725–741.
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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 | 0.033 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".