Optimal Sensor Locations for the Backward Lagrangian Stochastic Technique in Measuring Lagoon Gas Emission
Bibliographic record
Abstract
This study evaluated the impact of gas concentration and wind sensor locations on the accuracy of measuring gas emission rates from a lagoon environment using the backward Lagrangian stochastic (bLS) inverse-dispersion technique. Path-integrated concentrations (PICs) and three-dimensional (3D) wind vector data were collected at different locations within the lagoon landscape. A floating 45 m × 45 m perforated pipe network on an irrigation pond was used as a synthetic distributed emission source for the controlled release of methane. A total of 961 15-min datasets were collected under different atmospheric stability conditions over a 2-yr period. The PIC location had a significant impact on the accuracy of the bLS technique. The location of the 3D sonic anemometer was generally not a factor for the measured accuracies with the PIC positioned on the downwind berm. The PICs across the middle of the pond consistently produced the lowest accuracy with any of the 3D anemometer locations (<69% accuracy). The PICs located on the downwind berm consistently yielded the best bLS accuracy regardless of whether the 3D sonic anemometer was located on the upwind, side, or downwind berm (accuracies ranged from 79 to 108%). The accuracies of the emission measurements with the berm PIC-berm 3D setting were statistically similar to that found in a more ideal homogeneous grass field. Considering the practical difficulties of setting up equipment and the accuracies associated with various sensor locations, we recommend that wind and concentration sensors be located on the downwind berm.
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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 | 0.001 | 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 | 0.000 | 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 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".