Decoding methane concentration in Alberta oil sands: A machine learning exploration
Bibliographic record
Abstract
Most activities associated with Alberta’s oil sands industry are widely recognized as a serious threat to the environment, particularly the emission of greenhouse gases; the industrial residue that accumulates in oil sands tailings ponds (OSTPs) has the potential to emit large quantities of methane. Mathematical modeling of these emissions, and hence deducing where and why high methane concentrations can be found, is often infeasible due to complex interactions between different sources of methane and lack of availability of appropriate data. Additionally, stationing advanced monitoring devices either inside or in the vicinity of methane emitting sources can be expensive, and may require permits that are hard to obtain. Interpretable machine learning techniques, coupled with existing data from weather monitoring stations, offer a cost-effective alternative approach for modeling and understanding methane emissions sources. We introduce a multi-step framework for finding the primary factors associated with higher methane concentrations, powered by machine learning models (such as random forest) trained on high dimensional datasets sourced from multiple weather monitoring stations located in the Lower Athabasca region. The proposed framework can predict methane concentration levels, illustrate the dependence between the important features and their impact on these levels, and (via the incorporation of wind data) uncover locations of methane sources. We use it to locate such sources in northeastern Alberta. We additionally use Shapley values to find that O 3 ’s relationship with methane concentration is consistently concave, while that of NO X changes from linear increase to a saturation function with increasing distance from OSTPs. This paper serves as a guide for building machine learning-driven models to estimate methane concentration in Alberta’s oil sands, or similar regions with methane-producing extractive industries. • We use interpretable machine learning to explain methane levels in northeast Alberta. • The relationship between NO X and methane changed with distance to tailings ponds. • Using our method with wind direction data revealed methane source locations. • Methane concentrations were higher in winter due to both natural and human factors. • Oil sands sources were associated with higher methane contribution than swamplands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".