Mobile autonomous methane monitoring stations for emission measurement
Bibliographic record
Abstract
Natural gas has been forecast to continue grow up to 30% for the next 40 years and will remain as a key energy source. Alongside this projected growth, both the government and the industry have committed to reduce emission reductions. A critical focus is fugitive emissions, which are related to leaks or unintended losses of methane from sources such as hydrocarbon production, processing, transport, storage, transmission and distribution. The need for measuring and monitoring these emissions has been recognised in significant environmental inquiries related to the gas industry, such as the Northern Territory Fracking Inquiry (Pepper et al. 2018) and required in section D of the NT Code of Practice. This study describes an autonomous emission monitoring station developed to address the challenge of characterising temporally varying fugitive methane emissions. It has been designed specifically to tolerate the Australian outback’s extreme climateswhile providing laboratory-grade measurements in real-time at locations where there will be no access to grid power and standard telecommunications. Preliminary results demonstrating the continuous real-time measurements of methane and ethane concentrations of temporally varying phenomena will be presented. Specifically, the detection of methane and ethane concentrations and temporal changes related to bushfire progress will be shown.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".