The airborne greenhouse gas observation systems MAMAP2D-Light and MAMAP2D – Characterization and performance assessment
Bibliographic record
Abstract
Remote sensing measurements of greenhouse gases from aircraft to detect and quantify greenhouse gas emissions began about 15 years ago. These measurements have been exploited to detect and quantify predominantly anthropogenic emissions. However, with new satellite systems targeting especially methane (CH4) emissions on different scales, high-precision airborne measurements are needed to validate these satellite systems and detect and quantify emissions too small to be detected from space-based sensors.For this, the MAMAP2D family of airborne passive imaging remote sensing instruments has been and is being built at the Institute of Environmental Physics of the University of Bremen. MAMAP2D-Light, the first of this family, is a lightweight, compact spectrometer measuring carbon dioxide (CO2) and CH4 enhancements in a short-wave infrared band around 1.6 µm with a spectral resolution of ~1.1 nm. It was flown successfully on a Diamond HK36 TTC-ECO motor glider aircraft of the Jade University of Applied Sciences in Wilhelmshaven and the High Altitude Long Range operations (HALO) aircraft of DLR during the COMET 2.0 Arctic campaign in Canada. The MAMAP2D instrument, the next biggest in the MAMAP2D family, covers the SWIR band with a higher spectral resolution and additionally contains a near-infrared channel covering O2 absorption around 760 µm for path-length correction and is currently assembled in the laboratory.In this poster, we will present the spectral characterization of the MAMAP2D-Light instrument as flown during the COMET 2.0 Arctic campaign and assess its performance for detecting local CH4 and CO2 gradients. Additionally, initial laboratory characterizations of the MAMAP2D breadboarding activity will be presented.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".