Simultaneous measurement of net nitrogen mineralization and denitrification rates in soil using nitrification inhibitor 3,5-dimethylpyrazole
Bibliographic record
Abstract
A practical means to quantify the response of the rates of net N mineralization and denitrification over a wide range of soil water contents is generally lacking. This study examined the potential to use a nitrification inhibitor (NI) assay system to simultaneously estimate the rates of net N mineralization and denitrification, and applied the NI assay to assess the effect of water content on net N mineralization and denitrification rates in two soils with contrasting soil texture. The compound 3,5-dimethylpyrazole (DMP) applied at a rate of 200 mg kg−1 was found to provide essentially complete inhibition of nitrification over the duration of the soil incubation for two soils with contrasting soil texture (clay loam vs. sandy loam) and over a range of soil water contents (35%, 55%, and 85% water-filled pore space). This allowed net N mineralization to be estimated as the accumulation of soil ammonium ([Formula: see text]) and of denitrification as the disappearance of added nitrate ([Formula: see text]). Addition of DMP resulted in a small increase in soil respiration rate but did not appear to influence the rate of net soil N mineralization. The NI assay provides a practical means to quantify the rates of net N mineralization and denitrification simultaneously over a wide range of soil water contents. The assay can be readily scaled up to routinely test multiple soils in an efficient manner, has limited material costs, and is also relatively simple to perform.
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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) | 0.001 | 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 | 0.001 |
| 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".