Reconstruction of 1,979 prokaryotic metagenome-assembled genomes from 37 global cave environments
Bibliographic record
Abstract
Cave microorganisms represent unique extremophiles that have evolved in isolated, nutrient-limited environments and harbor exceptional metabolic capabilities. However, knowledge of cave microbial diversity at genomic level remains limited. Previous studies have focused on individual caves and do not give a global picture. Here, we present the first prokaryotic cave metagenomic catalog from 37 geographical diverse cave environments. We employed an optimized genome reconstruction pipeline to recover 3,837 medium-to-high quality cave metagenome-assembled genomes (MAGs). These MAGs were dereplicated into 1,979 species-level representative clusters that spanned 67 phyla of Bacteria (n = 1,858) and Archaea (n = 121) domains. Classification of representative species showed that 98.7% did not match any existing genome taxonomy classification of named species at ≥ 95% average nucleotide identity (ANI). Most representative genomes harbored putative biosynthetic gene clusters (BGCs) (98.0%) and enzymatic antibiotic resistance genes (ARGs) (95.0%). This comprehensive MAGs catalog provides a foundational resource for exploring cave microbial diversity, secondary metabolism, and the evolutionary origins of antibiotic resistance in subterranean ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".