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Record W3153208676 · doi:10.1038/s41598-021-87610-3

Methane emissions from upstream oil and gas production in Canada are underestimated

2021· article· en· W3153208676 on OpenAlexafffundabout
Katlyn MacKay, Martin Lavoie, Évelise Bourlon, Emmaline Atherton, Elizabeth O’Connell, Jennifer Baillie, Chelsea Fougère, David Risk

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsSt. Francis Xavier UniversityMemorial University of Newfoundland
FundersEnvironment and Climate Change CanadaNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities AgencyPetroleum Technology Alliance CanadaDavid Suzuki Foundation
KeywordsUpstream (networking)Environmental scienceMethaneProduction (economics)Methane emissionsFossil fuelDownstream (manufacturing)Petroleum engineeringBusinessWaste managementComputer scienceBiologyEngineeringEcologyEconomicsTelecommunications

Abstract

fetched live from OpenAlex

Methane emissions were measured at 6650 sites across six major oil and gas producing regions in Canada to examine regional emission trends, and to derive an inventory estimate for Canada's upstream oil and gas sector. Emissions varied by fluid type and geographic region, with the heavy oil region of Lloydminster ranking highest on both absolute and intensity-based scales. Emission intensities varied widely for natural gas production, where older, low-producing developments such as Medicine Hat, Alberta showed high emission intensities, and newer developments in Montney, British Columbia showed emission intensities that are amongst the lowest in North America. Overall, we estimate that the Canadian upstream oil and gas methane inventory is underestimated by a factor of 1.5, which is consistent with previous studies of individual regions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.196
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations151
Published2021
Admission routes3
Has abstractyes

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