Habitability and Human Factors: Lessons Learned in Long Duration Spaceflight
Bibliographic record
Abstract
This study documents the investigation of qualitative habitability and human factors feedback provided by scientists, engineers, and crewmembers on lessons learned from the ISS Program. A thorough review and understanding of this data is critical in charting NASA's future path in space exploration. NASA has been involved in ensuring that the needs of crewmembers to live and work safely and effectively in space have been met throughout the ISS Program. Human factors and habitability data has been collected from every U.S. crewmember that has resided on the ISS. The knowledge gained from both the developers and inhabitants of the ISS have provided a significant resource of information for NASA and will be used in future space exploration. The recurring issues have been tracked and documented; the top 5 most critical issues have been identified from this data. The top 5 identified problems were: excessive onsrbit stowage; environment; communication; procedures; and inadequate design of systems and equipment. Lessons learned from these issues will be used to aid in future improvements and developments to the space program. Full analysis of the habitability and human factors data has led to the following recommendations. It is critical for human factors to be involved early in the design of space vehicles and hardware. Human factors requirements need to be readdressed and redefined given the knowledge gained during previous ISS and long-duration space flight programs. These requirements must be integrated into vehicle and hardware technical documentation and consistently enforced. Lastly, space vehicles and hardware must be designed with primary focus on the user/operator to successfully complete missions and maintain a safe working environment. Implementation of these lessons learned will significantly improve NASA's likelihood of success in future space endeavors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".