Performance, microbial growth and community interactions of iron-dependent denitrification in freshwaters
Bibliographic record
Abstract
Iron-dependent denitrification is a safe and promising technology for nitrogen removal in freshwaters. However, the understanding of microbial physiology and interactions during the process was limited. Denitrifying systems inoculated with freshwater samples were operated with and without iron(II) at a low C/N ratio for 54 days. Iron addition improved nitrogen removal. Batch experiments confirmed that microbially mediated reaction rather than abiotic reaction dominated during the process. Metagenomics recovered genomes of the five most abundant microorganisms, which accounted for over 99% of the community in every triplicate of the iron-based system. Based on codon usage bias, all of them were fast-growing organisms. The total abundance of fast-growing organisms was 38% higher in the system with iron than in the system without iron. Notably, the most abundant organism Diaphorobacter did not have enzymes for asparagine and aspartate biosynthesis, whereas Rhodanobacter could not produce serine and cobalamin. Algoriphagus and Areminomonas lost synthesis enzymes for more amino acids and vitamins. However, they could always obtain these growth-required substances from another microorganism in the community. The two-partner relationship minimized the limitation on microbial reproduction and increased community stability. Our results indicated that iron addition improved nitrogen removal by supplying electron donors, promoting microbial growth, and building up syntrophic interactions among microorganisms with timely communications. The findings provided new insights into the process, with implications for freshwater remediation.
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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) | 0.001 | 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 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".