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Record W2790106392 · doi:10.1525/elementa.284

Methane emissions from oil and gas production sites in Alberta, Canada

2018· article· en· W2790106392 on OpenAlexaboutno aff
Daniel Zavala‐Araiza, Scott C. Herndon, Joseph Roscioli, Tara I. Yacovitch, Matthew R. Johnson, David R. Tyner, Mark Omara, B. Knighton

Bibliographic record

VenueElementa Science of the Anthropocene · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMethaneMethane emissionsEnvironmental scienceFossil fuelNatural gasOil and natural gasMethane gasGreenhouse gasAtmospheric sciencesEnvironmental engineeringWaste managementChemistryGeologyOceanographyEngineering

Abstract

fetched live from OpenAlex

We performed ground-based measurements (downwind, site-wide characterization) of methane emissions from older light oil and natural gas production sites in Alberta, Canada (Red Deer region, 60 measured sites). We developed a distribution of site-based methane emissions and as previously found in production regions in the United States, a small fraction of the sites account for the majority of methane emissions: 20% of the sites emit three quarters of the methane from oil and gas production. Using empirically derived emission factors, we compared an estimate of regional methane emissions, to a top-down airborne-based measurement of the same region. The airborne measurement was 35% lower, though not statistically different (4,800 ± 3,200 vs. 3,100 ± 2,200 kg CH4 h–1). In Alberta, the majority of these oil and gas emissions go unreported under current reporting requirements. Effective mitigation will most likely require frequent monitoring to identify high-emitting sites as well as leaky components that we hypothesize are also a major contributor to emissions.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations94
Published2018
Admission routes1
Has abstractyes

Explore more

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