Tracing N2O from dairy processing sludge amended soil with visualizing microscale heterogeneity of NH3 and pH (Short Communication)
Bibliographic record
Abstract
Nitrous oxide (N 2 O) emissions from organic waste and animal slurry contribute to climate change and endanger our ecosystems. For the development of efficient mitigation technologies, in-depth knowledge of emission processes is needed. This can be obtained by non-destructive, temporal measurements of in-situ soil profiles and the transformation of ammonium (NH 4 + ) during events of emissions. Planar optode imaging is a non-destructive measuring method that can be used to visualize spatiotemporal changes of ammonia (NH 3 ) and pH in soil systems. In this study, soil amended with dairy processing sludge (DPS) was incubated in static chambers for 23 days, and GHG emissions, NH 3 concentrations and pH in the soil were measured simultaneously over time. The aim was to investigate the potential of applying different planar optodes to provide information that gives insight into processes of N 2 O emissions. The DPS was applied to the soil as a surface layer (SL), with untreated soil as a control (CK). We were able to measure N 2 O emissions while monitoring spatiotemporal changes of soil pH and NH 3 concentrations. The visualized microscale heterogeneity of the soil contributed to a better understanding of N 2 O emission processes. While technical challenges (e.g., humidity sensitivity of the NH 3 optode and airtightness of the chambers) still need to be overcome, the method is a promising non-destructive method to study soil processes after application of different types of soil amendments. • A method of combining NH 3 and pH optodes with static chambers for gas measurements was developed. • N 2 O emissions were determined while simultaneously monitoring in-situ soil NH 3 and pH. • Visualizing soil microscale heterogeneity helped better understand N 2 O emission processes. • This method has potential to be a prescreening tool for new fertilizers before field applications.
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".