Essential metals for nitrogen fixation in a free‐living N <sub>2</sub> ‐fixing bacterium: chelation, homeostasis and high use efficiency
Bibliographic record
Abstract
Biological nitrogen fixation, the main source of new nitrogen to the Earth's ecosystems, is catalysed by the enzyme nitrogenase. There are three nitrogenase isoenzymes: the Mo-nitrogenase, the V-nitrogenase and the Fe-only nitrogenase. All three types require iron, and two of them also require Mo or V. Metal bioavailability has been shown to limit nitrogen fixation in natural and managed ecosystems. Here, we report the results of a study on the metal (Mo, V, Fe) requirements of Azotobacter vinelandii, a common model soil diazotroph. In the growth medium of A. vinelandii, metals are bound to strong complexing agents (metallophores) excreted by the bacterium. The uptake rates of the metallophore complexes are regulated to meet the bacterial metal requirement for diazotrophy. Under metal-replete conditions Mo, but not V or Fe, is stored intracellularly. Under conditions of metal limitation, intracellular metals are used with remarkable efficiency, with essentially all the cellular Mo and V allocated to the nitrogenase enzymes. While the Mo-nitrogenase, which is the most efficient, is used preferentially, all three nitrogenases contribute to N₂ fixation in the same culture under metal limitation. We conclude that A. vinelandii is well adapted to fix nitrogen in metal-limited soil 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".