Investigating the Atmospheric Microbial Ecosystem Through Theory, Bioenergetics, and Numerical Modeling: A Breath of Fresh Air for Aeromicrobiology
Bibliographic record
Abstract
Abstract The atmosphere may constitute the Earth's largest microbial ecosystem, yet it remains the least understood. While microorganisms can persist and may even thrive in the polyextremes of the Earth's atmosphere, it is still unknown whether the atmosphere sustains an active microbial community. Despite growing awareness of the role of the aeromicrobiome in shaping global biogeography, epidemiology, and climate, fundamental questions about its metabolic activity and ecological significance remain unanswered. Here, we outline how theoretical approaches and numerical modeling tools provide powerful avenues to investigate the atmospheric microbial ecosystem, offering unique insights that complement experimental and observational‐based studies and can overcome many of the challenges they face. We consider frameworks that integrate (a) theoretical considerations for microbial metabolism across a range of catabolic and anabolic processes, (b) microbial physiology and metabolic states, (c) thermodynamics and bioenergetics, (d) the chemical and physical conditions of the atmosphere and bioaerosols, (e) transport and residence time of microorganisms, and (f) bottom‐up and top‐down approaches. Theory and modeling‐based investigations into the aeromicrobiome can generate and test theory and model‐informed hypotheses, formulate mechanistic explanations of biological processes and observations, and inform targeted sampling strategies and experimentation. Together, these approaches bring us closer to determining whether the Earth's atmosphere is a true ecosystem—that is, a metabolically active community of organisms interacting with each other and with the environment. Advances in aeromicrobiology research brought about by theory and modeling can reveal significant insights into global biogeography, biogeochemical cycles, climate processes, and the limits for life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".