Microbial Ecology to Ocean Carbon Cycling: From Genomes to Numerical Models
Bibliographic record
Abstract
The oceans contain large reservoirs of inorganic and organic carbon and play a critical role in both global carbon cycling and climate. Most of the biogeochemical transformations in the oceans are driven by marine microbes. Thus, molecular processes occurring at the scale of single cells govern global geochemical dynamics, posing a challenge of scales. Understanding the processes controlling ocean carbon cycling from the cellular to the global scale requires the integration of multiple disciplines including microbiology, ecology, biogeochemistry, and computational fields such as numerical models and bioinformatics. A shared language and foundational knowledge will facilitate these interactions. This review provides the state of knowledge on the role marine microbes play in large-scale ocean carbon cycling through the lens of observational oceanography and biogeochemical models. We conclude by outlining ways in which the field can bridge the gap between -omics datasets and ocean models to understand ocean carbon cycling across scales. ▪ -Omic approaches are providing increasingly quantitative insight into the biogeochemical functions of marine microbial ecosystems. ▪ Numerical models provide a tool for studying global carbon cycling by scaling from the microscale to the global scale. ▪ The integration of -omics and numerical modeling generates new understanding of how microbial metabolisms and community dynamics set nutrient fluxes in the ocean.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".