Evaluating Soil Gas Migration Testing in the Energy Sector
Bibliographic record
Abstract
Canada’s energy sector accounts for approximately 44% of this country’s methane (CH4) emissions, with contributions identified to originate from production infrastructure and natural resource recovery processes. To reduce emissions and mitigate hazards such as asphyxiation, explosions, and groundwater contamination, provincial regulators [e.g., Alberta Energy Regulator (AER)] responsible for regulating the life cycle of oil and gas projects in Canada set guidelines for the identification of gases released from energy sector assets. Diverse field assessment techniques and instrumentation are used by energy sector stakeholders to identify point source emissions. Limited supporting information is available related to the comparative success of the different approaches deployed. This study characterized the reliability of field-deployable gas measurement instrumentation under laboratory and field settings and assessed the applications of these instruments for surface and subsurface gas migration (GM) testing approaches suggested by the AER. The primary focus was to evaluate the most widely available, cost-effective instrumentation, which measures combustible gases with a catalytic combustion detector (CCD). CCD reliability under ideal laboratory conditions was adequate across the concentrations and temperatures typically observed under field conditions. Field applications showed greater variability, likely due to the impact of environmental conditions such as soil moisture, soil compaction, and barometric pressure.
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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 | 0.003 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".