Seasonal dynamics of anaerobic oxidation of ammonium and denitrification in a dimictic lake during the stratified spring–summer period
Bibliographic record
Abstract
Abstract In aquatic ecosystems, nitrogen (N) loading is mitigated in redox transition zones principally through the processes of denitrification and anaerobic oxidation of ammonium (anammox). Here, we investigate the N cycling processes in the water column of a seasonal stratified lake influenced by benthic processes in Southern Germany (Fohnsee) during the development of the vertical redox stratification between April and September. Concentration profiles and stable isotope compositions of NO 3 − and NH 4 + together with numerical modeling and quantification of the hydrazine synthase gene ( hzsB ) and nitrite reductase ( nirK and nirS ) genes were used to identify the predominant nitrogen‐transformation processes at lake Fohnsee throughout the spring and summer periods. Water chemistry data, quantitative polymerase chain reaction analysis and increases of δ 15 N and δ 18 O values of nitrate from 7.0‰ to 41.0‰ and 2.0‰ to 28.0‰, respectively, showed that nitrate reduction to nitrite and NO occurs in an upward moving zone of the water column from June to September following the displacement of the oxycline caused by thermal stratification. We also observed an increase in δ 15 N of ammonium from 15‰ to 28‰ in the anoxic water column. Modeling results suggest that this shift in δ 15 N‐NH 4 + is predominantly controlled by mixing between ammonium stemming from the oxic water column with δ 15 N values of 25‰ and ammonium that is likely formed in the lake sediments by oxidation of organic matter with δ 15 N values of 11‰. Observed gene abundances ( hzsB , nirK , and nirS ) in lake water samples collected in June and July indicated the co‐occurrence of nitrate reduction and low rates of anammox, while the presence of sulfide in August and September may have inhibited the activity of anammox bacteria near the sulfate‐reduction zone at the lake bottom. This study revealed temporal and spatial (e.g., depth dependent) variations in the dominant N‐transformation processes in the investigated lake.
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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 | 0.000 | 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".