Analogs as a research platform: Systems-based optimization approach to facility selection
Bibliographic record
Abstract
NASA’s Artemis program and Moon to Mars objectives target development of a sustained human presence on the Moon. This drives the need for understanding what technologies and procedures are critical for long-duration surface habitation, in both a lunar and Martian environment. Earth-based analogs provide the ability to test mission components in comparable environments. However, there are a limited number of analog facilities, each with high-fidelity approximations of different features of the target environment. The limited availability of analog spots and associated high costs of testing make efficient use of analogs as a research platform critical to provide useful data for space exploration. Here, we present a review of analog platforms with a focus on surface analog facilities. We propose a framework to evaluate the merit and need for testing a given experiment in different analog facilities. Multiple criteria decision analysis techniques utilizing analytic hierarchy process calculations are used to evaluate the compatibility of each analog facility’s level of feature approximation with a given experiment or mission profile. 24 simulation features such as terrain, isolation, mission control, and available technology are used to evaluate facility approximation and analog needs of researchers. The experimental value, or merit, of a given experiment is quantified according to NASA’s targeted knowledge gaps as ranked in the Science Technology Mission Directorate’s shortfall list and the Human Research Program risk assessment, and the proposed technology/human readiness level increase resulting from the experiment. This selection methodology framework quantifies the merit of conducting a given experiment in various analog environments. It enables analog selection by researchers to target the best possible facility for their work, and enhances the ability of facilitators to select experiments that utilize the unique capabilities of their analog. The efficient use of analog resources via optimized experiment and facility selection will enable improved and rapid advancements to the technologies deemed most critical to test prior to in-situ integration.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".