Rationale and Proposed Design for a Mars Sample Return (MSR) Science Program
Bibliographic record
Abstract
The Mars Sample Return (MSR) Campaign represents one of the most ambitious scientific endeavors ever undertaken. Analyses of the martian samples would offer unique science benefits that cannot be attained through orbital or landed missions that rely only on remote sensing and in situ measurements, respectively.As currently designed, the MSR Campaign comprises a number of scientific, technical, and programmatic bodies and relationships, captured in a series of existing and anticipated documents. Ensuring that all required scientific activities are properly designed, managed, and executed would require significant planning and coordination. Because there are multiple scientific elements that would need to be executed to achieve MSR Campaign success, it is critical to ensure that the appropriate management, oversight, planning, and resources are made available to accomplish them. This could be achieved via a formal MSR Science Management Plan (SMP).A subset of the MSR Science Planning Group 2 (MSPG2)-termed the SMP Focus Group-was tasked to develop inputs for an MSR Campaign SMP. The scope is intended to cover the interface to the Mars 2020 mission, science elements in the MSR flight program, ground-based science infrastructure, MSR science opportunities, and the MSR sample and science data management.In this report, a comprehensive MSR Science Program is proposed that comprises specific science bodies and/or activities that could be implemented to address the science functionalities throughout the MSR Campaign. The proposed structure was designed by taking into consideration previous management review processes, a set of guiding principles, and key lessons learned from previous robotic exploration and sample return missions.
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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 | 0.065 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".