Factors regulating nitrification in aquatic sediments: effects of organic carbon, nitrogen availability, and pH
Bibliographic record
Abstract
We investigated the response in nitrification to organic carbon (C) availability, the interactive effects of the C: nitrogen (N) ratio and organic N availability, and differing pH in sediments from several streams in the upper midwestern United States. In addition, we surveyed 36 streams to assess variability in sediment nitrification rates. Labile dissolved organic carbon (DOC) additions of 30 mg C·L 1 (as acetate) to stream sediments reduced nitrification rates (P < 0.003), but lower concentration additions or dilution of ambient DOC concentration had no effect on nitrification. C:N and organic N availability strongly interacted to affect nitrification (P < 0.0001), with N availability increasing nitrification most at lower C:N. Nitrification was also strongly influenced by pH (P < 0.002), with maximum rates occurring at pH 7.5. A multiple regression model developed from the stream survey consisted of five variables (stream temperature, pH, conductivity, DOC concentration, and total extractable NH 4 + ) and explained 60% of the variation observed in nitrification. Our results suggest that nitrification is regulated by several variables, with NH 4 + availability and pH being the most important. Organic C is likely important at regulating nitrification only under high environmental C:N conditions and if most available C is relatively labile.
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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 | 0.001 |
| 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 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".