Fugitive Methane Emissions: Development of a Mobile High-Volume Sampling System
Bibliographic record
Abstract
Abstract Liquefied natural gas (LNG) has been considered as a substitute for diesel and heavy-fuel oil in heavy-duty trucks and marine vessels, respectively. However, the widespread adoption of LNG as a fuel is hampered by its uncertain potential to reduce greenhouse gas (GHG) emissions in comparison with diesel and heavy-fuel oil from the lifecycle standpoint. Methane is the main component of LNG and a potent GHG. In this study, the design and validation of a high-volume sampling (HVS) system are proposed to accurately measure methane emissions from the LNG fuel infrastructure, including experiment designs for calibration and system validation, and uncertainty analysis. The accuracy of HVS measurements is tested under controlled environment. The results indicate that the HVS system can quantify leak rates between 108 and 3,254 g/h with a maximum uncertainty of 10% as long as the distance between the leak source and the HVS system sampling port is maintained at less than 50 mm. In future work, the HVS system will be used to characterize methane emissions from LNG offloading or bunkering process, and update the GHG inventories in North America to fill the knowledge gap in the complete lifecycle analysis of LNG fuel for heavy-duty vehicles and marine vessels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".