In situ measurements of greenhouse gas mole fractions over North American high-latitude regions during CoMet 2.0 Arctic mission
Bibliographic record
Abstract
As a second most abundant anthropogenic greenhouse gas, methane has been an object of intense study over the past years. Despite a good overall knowledge of the sources contributing to the increase of its atmospheric abundance, the precise constrain of the global methane budget remains elusive, with large uncertainties still characterizing both anthropogenic and natural emissions, especially in the regions poorly constrained by observations, such as tropical wetlands or northern high-latitude regions.CoMet 2.0 Arctic mission, executed in August and September 2022 aimed at characterizing the distribution of CH4 and CO2 over significant regional sources with the use of a German Research Aircraft HALO (High Altitude Long-range Observatory), as well as to validate remote sensing measurements from state-of-the-art instrumentation installed on-board against a set of independent in-situ observations. These sources included both anthropogenic (oil, gas and coal industries) as well as natural sources that represent large-scale methane-emission regions (including wetlands and major deltas in the region).We will present results of in-situ observations performed across 15 research flights performed over a variety of environments. High-precision mole fractions of CO2, CH4 and CO were measured with the the JIG (Jena In-Situ Greenhouse gas sensor, based on Picarro 2401-m) instruments, while JAS (Jena Air Sampler) allowed collection of 155 spot samples to additionally characterize N2O, H2, SF6, O2/N2, Ar/N2 and stable isotopes (not presented).Flight strategies were adopted in order to balance the needs of different types of instrumentation aboard HALO aircraft, while simultaneously sample the atmospheric constituents in the optimal manner. These strategies included a) high- to mid-altitude horizontal legs during transfers from Base of Operations (Edmonton, Alberta) towards the specific target areas, b) local vertical profiles, c) low-altitude sections with detailed scanning of the mole fractions of atmospheric constituents across the PBL and residual layers. With the appropriate selection of strategies for targeting particular emission sources, we were able to gather a rich observation suite that demonstrates importance of interplay between regional fluxes and atmospheric dynamics on spatio-temporal ranges larger than usually considered.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".