Real-Time Measurement of Atmospheric CO2, CH4 and N2O above Rice Fields Based on Laser Heterodyne Radiometers (LHR)
Bibliographic record
Abstract
High-precision observations provide an efficient way to calculate greenhouse gas emissions from agricultural fields and their spatial and temporal distributions. Two high-resolution laser heterodyne radiometers (LHRs) were deployed in the suburb of Hefei (31.9°N 117.16°E) for the remote sensing of atmospheric CO2, CH4 and N2O above rice paddy fields. The atmospheric transmittance spectra of CO2, CH4 and N2O were measured simultaneously in real time, and the atmospheric total column abundance was retrieved from the measured data based on the optimal estimation algorithm, with errors of 0.7 ppm, 4 ppb and 2 ppb, respectively. From July to October, the abundance of CO2 in the atmospheric column that was influenced by emissions from rice fields increased by 0.7 ppm CH4 by 30 ppb, and by 4 ppb N2O. During the rice growth season, rice paddy fields play a role in carbon sequestration. CH4 and N2O emissions from paddy fields are negatively correlated. The method of baking rice paddy fields reduces CH4 emissions from rice fields, but N2O emissions from rice fields are usually subsequently increased. The measurement results showed that LHRs are highly accurate in monitoring atmospheric concentrations and have promising applications in monitoring emissions from rice paddy fields. In the observation period, rice paddy fields can sequester carbon, and CH4 and N2O emissions from rice fields are negatively correlated. The LHRs have strong application prospects for monitoring emissions from agricultural fields.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".