Impact of different chloride salts and their concentrations on nitrification and trace gas emissions from a sandy soil under a controlled environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 it