Global diversity and distribution of nitrogen-fixing bacteria in the soil
Bibliographic record
Abstract
Our knowledge of microbial biogeography has advanced in recent years, yet we lack knowledge of the global diversity of some important functional groups. Here, we used environmental DNA from 327 globally collected soil samples to investigate the biodiversity patterns of nitrogen-fixing bacteria by focusing on the nif H gene but also amplifying the general prokaryotic 16S SSU region. Globally, N-fixing prokaryotic communities are driven mainly by climatic conditions, with most groups being positively correlated with stable hot or seasonally humid climates. Among soil parameters, pH, but also soil N content were most often shown to correlate with the diversity of N-fixer groups. However, specific groups of N-fixing prokaryotes show contrasting responses to the same variables, notably in Cyanobacteria that were negatively correlated with stable hot climates, and showed a U-shaped correlation with soil pH, contrary to other N-fixers. Also, the non-N-fixing prokaryotic community composition was differentially correlated with the diversity and abundance of N-fixer groups, showing the often-neglected impact of biotic interactions among bacteria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".