A Wholistic Approach to Human-in-the-Loop Ecosystem
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0626.vid Space exploration is amongst the greatest endeavors of the humankind. It continues to fuel human curiosity and imagination, as the limits and boundaries continue to extend beyond the Low Earth Orbit to other destinations, such as Moon and Mars. Technological advancements and scientific discoveries continue to redefine the boundaries of the human body and mind, revealing remarkable resilience, cognitive, physical and psychological performance in austere environments. However, as human kind prepares to embark on deep-space missions, there are fundamentally new challenges and considerations that have to be addressed to ensure successful mission outcomes. Restricted space, increased communication delays and remoteness from Earth, with limited ability for emergency return, necessitate development of a comprehensive human-in-the-loop ecosystem, with increased autonomy and clinical decision-making capacity. The proposed research harnesses the potential of big data and streaming data analytics to support a paradigm shift from reactive to proactive health management in-flight. It demonstrates the potential to support prognostics, diagnostics and mitigation of medical contingencies in-flight through a meaningful and practical use of the acquired data to inform clinical decision making, significance of which is demonstrated within the context of adaption-based analytics in a ground-based study “Luna-2015”.
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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 | 0.004 |
| 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 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".