Combining Aerial and Ground Surveys to Quantify Oil and Gas Sector Methane Emissions in Colombia
Bibliographic record
Abstract
Curbing methane emissions is a crucial aspect of achieving emissions reduction targets across the world. This is particularly important in Colombia, where it is estimated that 24% of anthropogenic methane emissions originate from the energy sector (IEA, 2023). However, the mitigation potential remains hampered by a lack of understanding of emission sources in the field and limited access to accurate official inventories. The objective of this study is to develop a comprehensive inventory of methane emissions within the oil and gas industry in Colombia. The implemented framework consists of a hybrid inventory that integrates top-down, source-resolved aerial measurements with bottom-up measurements following the published methodology of Johnson et al., Comms. Earth & Environ, 2023. This approach not only facilitates a detailed attribution of emission sources but also quantifies the measurement and sample size uncertainties, employing the detection probability of the airborne sensor, Monte Carlo analysis, and bootstrap analysis. For this study, around 3,400 facilities were included in the top-down campaign, complemented by a select sample of facilities in a parallel bottom-up campaign. The total facility sample covers six different production regions across five departments, including a wide range of oil and gas facilities and production types. This presentation will discuss the initial results of the field campaigns and progress towards the completion of a first-ever measurement-based methane inventory for Colombia that is intended to be used to support verified reporting under the International Oil and Gas Methane Partnership (OGMP 2.0).
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".