Remote Science Support during MARS2013: Testing a Map-Based System of Data Processing and Utilization for Future Long-Duration Planetary Missions
Bibliographic record
Abstract
MARS2013 was an integrated Mars analog field simulation in eastern Morocco performed by the Austrian Space Forum between February 1 and 28, 2013. The purpose of this paper is to discuss the system of data processing and utilization adopted by the Remote Science Support (RSS) team during this mission. The RSS team procedures were designed to optimize operational efficiency of the Flightplan, field crew, and RSS teams during a long-term analog mission with an introduced 10 min time delay in communication between "Mars" and Earth. The RSS workflow was centered on a single-file, easy-to-use, spatially referenced database that included all the basic information about the conditions at the site of study, as well as all previous and planned activities. This database was prepared in Google Earth software. The lessons learned from MARS2013 RSS team operations are as follows: (1) using a spatially referenced database is an efficient way of data processing and data utilization in a long-term analog mission with a large amount of data to be handled, (2) mission planning based on iterations can be efficiently supported by preparing suitability maps, (3) the process of designing cartographical products should start early in the planning stages of a mission and involve representatives of all teams, (4) all team members should be trained in usage of cartographical products, (5) technical problems (e.g., usage of a geological map while wearing a space suit) should be taken into account when planning a work flow for geological exploration, (6) a system that helps the astronauts to efficiently orient themselves in the field should be designed as part of future analog studies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".