Systemic Genome Correlation Loss as a Central Characteristic of Spaceflight
Bibliographic record
Abstract
Abstract Space exploration has captured the imagination of humanity for generations. From the first steps on the moon to the recent Mars rover and Artemis lunar exploration missions, space travel has always been an ambitious goal for humanity. However, as we venture further into space and prepare for long-term missions to other planets, the physiological and health risks associated with prolonged space travel are becoming more prominent. Most current research on astronaut health focuses on identifying individual genes or pathways for specific symptoms astronauts face. The human system is complex and delicate, and the effects of microgravity, radiation, and isolation on astronaut health during long-duration spaceflight are still not fully understood. This study used a novel ranking and analysis methodology to combine space omics data from multiple datasets in the NASA OSDR repository. The data was used to generate a multi-omic, integrative bioinformatics analysis pipeline, which identified and characterized a genome-wide spaceflight gene expression correlation loss as a central biosignature for astronaut health on the International Space Station (ISS). Our findings indicate that genome-wide correlation loss corresponds to a breakdown in gene synchronization and cooperation, showcasing the systemic symptoms spaceflight induces and their genomic roots.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".