Optical Sensor Fusion for Identification and Visualization of Fugitive Greenhouse Gas Emissions
Bibliographic record
Abstract
This paper presents a sensor fusion between three non-gas sensing optical devices, to present a low-cost and accessible alternative to optical gas imaging systems used in detecting fugitive greenhouse gas (GHG) emissions in the wastewater sector. Gas visualization and quantification require sensors that fall within the absorption spectrum of a target greenhouse gas, that the environment has a sufficient background temperature difference, and that the gas is captured in the frame. We propose using three optical-based sensors; a Hyperspectral Camera, FLIR Thermal Camera, and a Spectroradiometer as an integrated system to identify and visualize gases. The cameras were mounted on a gimbal for stabilization and a custom 3D printed mount was developed to hold all three sensors at the same focal point. A custom-built acrylic chamber with variable flow speeds and a blacked-out background was used for controlled methane (CH4) and nitrous oxide (N2O) releases to test the validity of the integrated system. Results showed the uncooled thermal camera can identify methane and nitrous oxide leaks, with the radiometer able to capture the methane absorption spectrum. The hyperspectral camera, however, was unable to capture gas images, future work will require a full-frame camera with a higher spectral range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".