Non-cyanobacterial diazotrophs: global diversity, distribution, ecophysiology, and activity in marine waters
Bibliographic record
Abstract
Biological dinitrogen (N2) fixation supplies nitrogen to the oceans, supporting primary productivity, and is carried out by some bacteria and archaea referred to as diazotrophs. Cyanobacteria are conventionally considered to be the major contributors to marine N2 fixation, but non-cyanobacterial diazotrophs (NCDs) have been shown to be distributed throughout ocean ecosystems. However, the biogeochemical significance of marine NCDs has not been demonstrated. This review synthesizes multiple datasets, drawing from cultivation-independent molecular techniques and data from extensive oceanic expeditions, to provide a comprehensive view into the diversity, biogeography, ecophysiology, and activity of marine NCDs. A NCD nifH gene catalog was compiled containing sequences from both PCR-based and PCR-free methods, identifying taxa for future studies. NCD abundances from a novel database of NCD nifH-based abundances were colocalized with environmental data, unveiling distinct distributions and environmental drivers of individual taxa. Mechanisms that NCDs may use to fuel and regulate N2 fixation in response to oxygen and fixed nitrogen availability are discussed, based on a metabolic analysis of recently available Tara Oceans expedition data. The integration of multiple datasets provides a new perspective that enhances understanding of the biology, ecology, and biogeography of marine NCDs and provides tools and directions for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".