Data‐driven identification of major axes of functional variation in bacteria
Bibliographic record
Abstract
The discovery of major axes of correlated functional variation among species and habitats has revealed the fundamental trade-offs structuring both functional and taxonomic diversity in eukaryotes such as plants. Whether such functional axes exist in the bacterial realm and whether they could explain bacterial taxonomic turnover among ecosystems remains unknown. Here, we use a data-driven approach to leverage global genomic and metagenomic datasets to reveal the existence of major axes of functional variation explaining both evolutionary differentiation within Bacteria and their ecological sorting across diverse habitats. We show that metagenomic variation among bacterial communities from various ecosystems is structured along a few axes of correlated functional pathways. Similar clusters of traits explained phylogenetic trait variation among >16,000 bacterial genomes, suggesting that functional turnover among bacterial communities from distinct habitats does not only result from the differential filtering of similar functions among communities, but also from phylogenetic correlations among these functions. Concordantly, functional pathways associated with trait clusters that were most important for defining functional turnover among bacterial communities were also those that had the highest phylogenetic signal in the bacterial genomic phylogeny. This study overall underlines the important role of evolutionary history in shaping contemporary distributions of bacteria across ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".