Application of Cross-Track Infrared Sounder (CrIS) Instrument for Remote Detection of Agricultural NH3 Emissions over Netherlands
Bibliographic record
Abstract
Satellite observations have great potential for monitoring emissions and concentrations of atmospheric species. This is especially true for atmospheric ammonia (NH3), which varies greatly in space and time and is difficult to measure in-situ due to its sticky nature. NH3 measurements are important as NH3 is a significant contributor to the production of secondary inorganic aerosols (PM2.5) and can add excessive reactive nitrogen to the environment. In this study we demonstrate how satellite remote sensing observations can be used to monitor changes in NH3 concentrations by evaluating timeseries of Cross-Track Infrared Sounder (CrIS) satellite data with in-situ NH3 concentrations and meteorological parameters (i.e. soil temperature and soil moisture). We provide an example demonstrating the capability to monitor the annual springtime increase in atmospheric NH3 concentrations in Netherlands, which is mainly associated with farming practices (e.g. manure spreading on fields in the springtime). We then combine these satellite observations of NH3 with meteorological conditions, with the goal of developing a model to predict the timing of ammonia emissions based on past agricultural practices in the area (e.g. artificial fertilizer and manure spreading).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".