Controls on the Isotopic Composition of Nitrite (δ15N and δ18O) during Denitrification in Freshwater Sediments
Bibliographic record
Abstract
The microbial reduction of nitrate, via nitrite into gaseous di-nitrogen (denitrification) plays a major role in nitrogen removal from aquatic ecosystems. Natural abundance stable isotope measurements can reveal insights into the dynamics of production and consumption of nitrite during denitrification. In this study, batch experiments with environmental bacterial communities were used to investigate variations of concentrations and isotope compositions of both nitrite and nitrate under anoxic conditions. To this end, denitrification experiments were carried out with nitrite or nitrate as sole electron acceptors at two substrate levels respectively. For experiments with nitrate as substrate, where the intermediate compound nitrite is both substrate and product of denitrification, calculations of the extent of isotope fractionation were conducted using a non-steady state model capable of tracing chemical and isotope kinetics during denitrification. This study showed that nitrogen isotope fractionation was lower during the use of nitrite as substrate (ε = -4.2 and -4.5‰ for both treatments) as compared to experiments where nitrite was produced as an intermediate during nitrate reduction (ε = -10 and -15‰ for both treatments). This discrepancy might be due to isotopic fractionation within the membrane of denitrifiers. Moreover, our results confirmed previously observed rapid biotic oxygen isotope exchange between nitrite and water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".