Evaluation of a Gaussian dispersion transformation technique for tomographic mapping of the concentration field of atmospheric chemicals using multi-path optical remote sensing
Bibliographic record
Abstract
Horizontal radial plume mapping is a cost-effective optical remote sensing method for sensitive mapping concentration distribution of atmospheric chemicals in real time. However, its sparse sampling poses challenges for reconstruction algorithms. Neither non-smooth nor smooth algorithms can recover the realistic plume shape. A new approach called Gaussian dispersion transformation (GDT) has been proposed. It first reconstructs the emission rates from unknown sources. Then concentrations are calculated through a transformation matrix defined by a Gaussian dispersion model. Smoothness regularization is also applied during the reconstruction. The method was evaluated by using randomly generated maps. It shows significant improvement over a reconstructed plume shape. The nearness shows 72%-117% better than the non-negative least-square (NNLS) algorithm and 15%-26% better than the low third derivative (LTD) algorithm. A controlled-release field experiment of methane was also conducted. The realistic concentration distribution was calculated by using a Lagrangian stochastic dispersion model. The GDT algorithm successfully recovered the realistic plume shape. The nearness shows approximately 16% better than the NNLS and the LTD algorithms. Finally, a sensitivity analysis shows that the wind direction and atmospheric stability are the main parameters that affect the performance of the GDT algorithm.
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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".