Biogeography of anaerobic ammonia-oxidizing (anammox) bacteria
Bibliographic record
Abstract
Anaerobic ammonia-oxidizing (anammox) bacteria are able to oxidize ammonia and reduce nitrite to produce N2 gas. After being discovered in a wastewater treatment plant (WWTP), anammox bacteria were subsequently characterized in natural environments, including marine, estuary, freshwater, and terrestrial habitats. Although anammox bacteria play an important role in removing fixed N from both engineered and natural ecosystems, broad scale anammox bacterial distributions have not yet been summarized. The objectives of this study were to explore global distributions and diversity of anammox bacteria and to identify factors that influence their biogeography. Over 6000 anammox 16S rRNA gene sequences from the public database were analyzed in this current study. Data ordinations indicated that salinity was an important factor governing anammox bacterial distributions, with distinct populations inhabiting natural and engineered ecosystems. Gene phylogenies and rarefaction analysis demonstrated that freshwater environments and the marine water column harbored the highest and the lowest diversity of anammox bacteria, respectively. Co-occurrence network analysis indicated that Ca. Scalindua strongly connected with other Ca. Scalindua taxa, whereas Ca. Brocadia co-occurred with taxa from both known and unknown anammox genera. Our survey provides a better understanding of ecological factors affecting anammox bacterial distributions and provides a comprehensive baseline for understanding the relationships among anammox communities in global environments.
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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) | 0.000 | 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 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".