Astrovirology: how viruses will enhance our understanding of life in the universe
Bibliographic record
Abstract
Viruses are the most numerically abundant biological entities on Earth. As ubiquitous replicators of information molecules and agents of community change, viruses have potent effects on life on Earth and may play a critical role in human spaceflight missions, life detection missions to other planetary bodies, and in planetary protection. However, major knowledge gaps constrain our understanding of the Earth’s virosphere: 1) the role viruses play in biogeochemical cycles, 2) the origin(s) of viruses, and 3) the involvement of viruses in the evolution, distribution, and persistence of life. As viruses are the only replicators that span all known types of nucleic acids, an expanded experimental and theoretical toolbox built for Earth’s viruses will be pivotal for detecting and understanding life on Earth and beyond. Only by filling in these knowledge and technical gaps will we obtain an inclusive assessment of how to distinguish and detect life on other planetary surfaces. Meanwhile, space exploration requires life-support systems for the needs of humans, plants, and their microbial inhabitants. Viral effects on microbes and plants are essential for Earth’s biosphere and human health, but virus-host interactions in spaceflight are poorly understood. Viral relationships with their hosts respond to environmental changes in complex ways which are difficult to predict by extrapolating from Earth-based proxies. These relationships must therefore be studied in space to fully understand how spaceflight will modulate viral impacts on human health and life-support systems, including microbiomes. This review addresses key questions that must be examined to incorporate viruses into Earth system models, life-support systems, and life detection. Further, the results of tackling these questions will help in our efforts to develop planetary protection protocols and further our understanding of viruses in astrobiology.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".