A Wholistic Approach to Human-in-the-Loop Ecosystem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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”.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it