Optical Chemical Sensors for Soil Analysis: Possibilities andChallenges of Visualizing NH3 Concentrations as well as pH and O2 Microscale Heterogeneity
Bibliographic record
Abstract
Agricultural nitrogen (N) application to soils is the main source of atmospheric ammonia (NH3) emissions. Ammonia negatively impacts the environment on a large scale. These emissions are affected by spatiotemporal heterogeneities of parameters within the soil on a microscale. Some key parameters controlling processes of the N cycle are soil oxygen (O2) and pH. To better understand biogeochemical soil processes and NH3 emissions we propose the application of optical chemical sensors (optodes) in soils. The use of optodes in soil science is in its infancy. In this study, we investigated the possibilities and challenges of using optodes in non-waterlogged soils with the extended application of a recently developed NH3 optode in combination with pH and O2 optodes in two different soils and with different fertilizers. Our results demonstrated the possibility to visualize reductions of NH3 concentrations by 76 % and 87 % from the incorporation of sludge compared to the surface application of sludge. We showed in 2D how soil pH and fertilizer composition correlate with NH3 volatilization. Our measurements revealed that pH optodes show certain advantages over conventional methods when measuring pH in soils in-situ. Lastly, we investigated spatiotemporal dynamics of O2 at different soil water contents and discussed potential challenges, which can lead to measuring artifacts.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".