Evaluating the reliability of UAV-based carbon dioxide measurements in the lower troposphere
Bibliographic record
Abstract
Vertical profiles of carbon dioxide (CO 2 ) were captured in rural Ontario, Canada, using an Aeroqual Series 500 monitor mounted onboard an Uncrewed Aerial Vehicle (UAV). Measurements at various altitudes within the Transport Canada regulations height limit (5 m – 95 m) were compared to simultaneous measurements taken by an identical sensor at ground-level. Results demonstrate a high degree of agreement between onboard and ground-level observations, with a mean different of -6.02 ppm, and the majority of differences falling within the sensor’s factory calibration accuracy (±10 ppm + 5%). The study also demonstrates the ability to capture the homogeneity of CO 2 in the lower troposphere, with Intraclass Correlation Coefficients (ICC) exceeding 0.75 at all altitudes, without the addition of turbulence by UAV flight. The vertical profiles captured in this study validate the use of UAV-based measurements for understanding the distribution and transport of greenhouse gases near the surface of the Earth. This research has implications for routine air quality monitoring to improve atmospheric models, environmental impact assessments and the development of targeted emission reduction strategies. • Vertical profiles of CO2 were captured at two sites in rural Ontario, Canada, using UAV-mounted handheld monitors. • UAV-based measurements showed strong agreement across various altitudes (5 m to 95 m) with simultaneous ground-level measurements taken by an identical sensor. • Study confirms UAV-based monitoring can reliably capture the homogeneity of CO2 concentrations in the lower troposphere.
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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 | 0.003 | 0.001 |
| 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".