Comment on amt-2023-193
Bibliographic record
Abstract
Abstract. Atmospheric methane measurements are important for evaluating high resolution methane inventories and monitoring emissions reductions. Despite recent international efforts to harmonize measurement methodologies and techniques, currently there are no standardized or internationally accepted techniques for estimating emissions from mobile in situ concentration measurements. We present measurements from two different mobile in situ methane laboratories, and compare emission rates calculated from four Gaussian plume Bayesian optimal estimation strategies and a statistical algorithm. For mobile transects from the slower flow-rate instrument, we find a significant asymmetric smoothing artifact. The effect of this asymmetry is most significant for short transects of small (0–50 kg CH4 day−1), nearby methane sources, where the plume crossing time is comparable to the mean residence time of the instrument. We develop a model of this effect, demonstrate how this model can be applied to Gaussian plume inversions, and describe its limitations. We use these results to compute emissions rate estimates for two methane sources from Toronto’s wastewater management system to demonstrate the use and limitations of Gaussian plume inversions to quantify methane emissions in an urban environment. Overall, we highlight the importance of using observed plume enhancement areas rather than the more commonly used enhancement heights for determining comparable emissions estimates between different mobile laboratories.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.036 | 0.020 |
| Insufficient payload (model declined to judge) | 0.091 | 0.097 |
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".