Impact of different chloride salts and their concentrations on nitrification and trace gas emissions from a sandy soil under a controlled environment
Bibliographic record
Abstract
Abstract Potassium chloride (KCl) and magnesium chloride (MgCl 2 ) can be used to reduce carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) emissions, but their efficacy can be strongly affected by their Cl − concentration. This study aimed to compare the behaviour of different Cl − salts and concentrations with that of a typical commercial nitrification inhibitor (3, 4‐dimethylpyrazole phosphate, PIADIN). KCl, MgCl 2 and PIADIN were investigated under a laboratory incubation experiment for two months. KCl and MgCl 2 were applied at 0.5 and 1.0 g kg −1 , while PIADIN was applied at 25 mg kg −1 soil. CO 2 and N 2 O concentrations were analysed during the incubation period. The and dynamics in soil were also measured. The results showed 0.5 and 1.0 g kg −1 KCl and 0.5 g kg −1 MgCl 2 decreased CO 2 ‐C emissions by 43%–46% and increased N 2 O‐N emissions by 15%–48%, whereas 1.0 g kg −1 MgCl 2 decreased CO 2 ‐C emissions by 72% and N 2 O‐N emissions by 19%. KCl and MgCl 2 retarded the decrease of the ‐N concentration and increase of the ‐N concentration. PIADIN reduced the emissions of CO 2 ‐C by 113% and N 2 O‐N by 97% and maintained a high soil ‐N concentration and low ‐N concentration. MgCl 2 addition at 1.0 g kg −1 was an effective treatment as the Mg both fertilized the soil and inhibited CO 2 ‐C and N 2 O‐N emissions. Moreover, 1.0 g kg −1 MgCl 2 could retard soil nitrification, the decrease of ‐N concentration and the increase of ‐N concentration. While PIADIN had no fertilizing value, it was a more effective nitrification inhibitor than Cl − salts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".