Developing Intra-EVA Science Support Team Practices for a Human Mission to Mars
Bibliographic record
Abstract
During the BASALT research program, real (nonsimulated) geological and biological science was accomplished through a series of extravehicular activities (EVAs) under simulated Mars mission conditions. These EVAs were supported by a Mission Support Center (MSC) that included an on-site, colocated Science Support Team (SST). The SST was composed of scientists from a variety of disciplines and operations researchers who provided scientific and technical expertise to the crew while each EVA was being conducted (intra-EVA). SST management and organization developed under operational conditions that included Mars-like communication latencies, bandwidth constraints, and EVA plans that were infused with Mars analog field science objectives. This paper focuses on the SST workspace considerations such as science team roles, physical layout, communication interactions, operational techniques, and work support technology. Over the course of BASALT field deployments to Idaho and Hawai'i, the SST team made several changes of note to increase both productivity and efficiency. For example, new roles were added for more effective management of technical discussions, and the layout of the SST workspace evolved multiple times during the deployments. SST members' reflexive adjustments resulted in a layout that prioritized face-to-face discussions over face-to-data displays, highlighting the importance of interpersonal communication during SST decision-making. In tandem with these workspace adjustments, a range of operational techniques were developed to help the SST manage discussions and information flow under time pressure.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".