Characterization of nitrogen isotope fractionation during nitrification based on a coastal time series
Bibliographic record
Abstract
Abstract Isotopic enrichment factors are key to using stable isotope signatures in biogeochemical studies. However, these are typically determined in laboratory experiments and their applicability to environmental conditions is difficult to test. Here, we analyzed nitrogen stable isotope changes associated with nitrification in a coastal basin using weekly time‐series measurements of δ15N in particulate nitrogen, ammonium, nitrite, and nitrate. Two year‐long time series were selected as contrasting natural experiments in the ammonium‐rich, aphotic bottom water of Bedford Basin, Nova Scotia, Canada. In 2014, ammonia oxidation (AO) was associated with Thaumarchaeota and nitrite concentrations remained low (< 0.5 μmol kg−1). In contrast, transient nitrite accumulation (~ 8 μmol kg−1) and a more rapid δ15NNH4 increase in the fall of 2017 were likely caused by ammonia‐oxidizing bacteria, associated with higher AO rates and, possibly, stronger nitrogen‐isotope enrichment (15εAO). Estimates of 15εAO (21.8 ± 2.2‰, 24.1 ± 1.1‰) were derived empirically using Rayleigh models applied to field data from restricted periods during which the bottom waters approximated a closed system and influence on 15εAO from other processes was demonstrably insignificant. Using a numerical reactive‐transport model, we found that the best fit for the δ15N data was obtained with 15εAO values (18.9‰, 25.1‰) close to those determined by the Rayleigh models. The time series also revealed substantial (~ 7‰) 15N‐enrichment of the particulate nitrogen due to light‐independent assimilation of partially nitrified ammonium. Consistent with previous studies, these field‐based nitrogen isotope fractionation experiments suggest that the range of 15εAO values relevant for marine systems may be narrower than determined in laboratory studies.
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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".