Networking Mapping and Emissions Analysis of the Alberta Gas Network
Bibliographic record
Abstract
Abstract Firms are increasingly asked to understand the downstream effects of their products and processes. This includes their CO2 emissions, which are made up of direct, indirect and end-use emissions. Firms often have good measures of the direct (Scope 1) emissions but less accurate measures of their indirect (Scope 2 and 3) emissions. Moreover, they may not have estimates of other firms’ operations and thus may struggle with comparative analysis. As the fifth largest producer of natural gas in the world, Canada's publicly available petroleum datasets are invaluable in providing insight into natural gas production, processing, and transmission, as well as the associated emissions. This includes the Petrinex dataset. Unfortunately, Petrinex does not track production from well to sales location, it reports data to and from each element. Moreover, there is a large number of pipeline interconnections and facilities with flows and connections varying dynamically through time. This makes for a complex system to analyse and interpret at the network level, especially so given the need for monthly updates and cross-validation. We have analysed the Petrinex dataset using network/graph database techniques and combined the results with publicly reported GHG emissions data. This has resulted in a model that tracks emissions along the midstream and processing infrastructure in ways previously impossible. We believe this provides a deeper understanding of the flow rates and emissions related to the connections, nodes, processing steps and sales points in the gas transportation infrastructure of a major producing area. This paper presents some of the early findings of our work: long-term and seasonal trends; comparisons between regions, resource development targets; distributions of network and emissions metrics; and evidence as to whether or not industry is capable of reducing emissions in a mature development basin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".