PHM for astronauts: Elaborating and refining the concept
Bibliographic record
Abstract
Clarifying and evolving the “PHM for Astronauts” concept, introduced in [1], this conceptual paper focuses on particular PHM-based solutions to bring Human Health and Performance (HH&P) technologies to the required technology readiness level (TRL) in order to mitigate the HH&P risks of manned space exploration missions. This paper discusses the particular PHM-based solutions for some HH&P technologies that are, namely by NASA designation, the Autonomous Medical Decision technology and the Integrated Biomedical Informatics technology. Both of the technologies are identified as essential ones in NASA's integrated technology roadmap for the Technology Area 06: Human Health, Life Support, and Habitation Systems. The proposed technology solutions are to bridge PHM, an engineering discipline, to HH&P domain in order to mitigate the risks by focusing on efforts to reduce countermeasure mass and volume and drive the risks down to an acceptable level. The Autonomous Medical Decision technology is based on wireless handheld devices and is a result of a paradigm shift from tele-medicine to that of health support autonomy. The Integrated Biomedical Informatics technology is based on Crew Electronic Health Records (CEHR) system with predictive diagnostics capability developed for crew members rather than for healthcare professionals. The paper explores the proposed PHM-based solutions on crew health maintenance in terms of predictive diagnostics providing early and actionable real-time warnings of impending health problems that otherwise would have gone undetected.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".