Editorial: Exploring the diversity, ecological significance, and systematics of uncultivated prokaryotic taxa
Bibliographic record
Abstract
these organisms exist, along with their potential predisposition for cultivation using conventional methods, have made the study of these uncultivated prokaryotes a critical frontier in microbiology, unveiling extensive and previously unexplored microbial diversity (Rinke et al., 2013;Huber et al., 2007;Parks et al., 2017;Lloyd et al., 2018). Despite significant challenges, recent advancements in omics technologies have provided invaluable insights into the ecological roles, systematics, and metabolic capacities of these elusive microorganisms (Garza and Dutilh, 2015;Lewis et al. 2021). Traditionally, many prokaryotes have been considered difficult or even impossible to culture using conventional methods, discouraging many microbiologists from studying them and leaving much of the microbial diversity undiscovered. However, innovative approaches leveraging metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) are now shedding light on their biology, potentially guiding the development of tailored cultivation strategies and challenging the longheld paradigm of 'uncultivability.' These techniques significantly broaden our understanding of microbial diversity, ecological interactions and roles, and evolutionary history (Gutleben et al., 2018;Lewis and Ettema, 2019;Lewis et al., 2021;Xie et al., 2021;Liu et al., 2022;Laugier, 2023). Coupled with innovative culturing techniques such as co-cultivation strategies, microfluidics, and synthetic biology, these developments are establishing culturomics as a valuable complement to omics-based studies. Further pushing the boundaries of microbial discovery is essential for unraveling the functioning of complex microbial communities that have long been unknown or have evaded traditional cultivation methods (Lagier et al., 2012; The seven studies presented in this topic highlight roles of uncultivated
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.033 | 0.029 |
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".