A Strategic Framework to Support Scientific Communications for Mars Sample Return Science: Overview and Status
Bibliographic record
Abstract
The joint NASA/ESA Mars Sample Return (MSR) Campaign is a cornerstone of both agencies’ long-term scientific exploration strategy that would revolutionize our understanding of the history of Mars, the Solar System, and the potential for life beyond Earth. In 2023, findings from an Independent Review Board (IRB-2) emphasized the need for clear and compelling communication of MSR’s scientific and strategic value to Congress, the scientific community, and the public [1]. In response, NASA’s Science Mission Directorate’s MSR IRB-2 Response Team (MIRT) acknowledged the critical need for strengthening and enhancing strategic communications to ensure mission success and public support [2].
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".