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Record W4385768408 · doi:10.1093/pnasnexus/pgad260

Estimation of natural methane emissions from the largest oil sand deposits on earth

2023· article· en· W4385768408 on OpenAlexafffundabout
Cao Wei, Seyed Mostafa Jafari Raad, Hassan Hassanzadeh

Bibliographic record

VenuePNAS Nexus · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilUniversity of Calgary
KeywordsMethaneOil sandsEnvironmental scienceMethane emissionsNatural gasFossil fuelMonte Carlo methodGreenhouse gasAtmospheric sciencesPetroleum engineeringEnvironmental engineeringHydrology (agriculture)GeologyWaste managementChemistryEngineeringGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

Abstract Worldwide methane emission by various industrial sources is one of the important human concerns due to its serious climate and air-quality implications. This study investigates less-considered diffusive natural methane emissions from the world's largest oil sand deposits. An analytical model, considering the first-order methane degradation, in combination with Monte Carlo simulations, is used to quantitatively characterize diffusive methane emissions from Alberta's oil sands formations. The results show that the average diffusive methane emissions from Alberta's oil sands formations is 1.56 × 10−4 kg/m2/year at the 90th percentile of cumulative probability. The results also indicate an annual diffusive methane emissions rate of 0.857 ± 0.013 Million tons of CO2e/year (MtCO2e/year) from Alberta's oil sands formations. This finding suggests that natural diffusive leakages from the oil sands contribute an additional 1.659 ± 0.025 and 5.194 ± 0.079% to recent Canada's 2019 and Alberta's 2020 methane emission estimates from the upstream oil and gas sector, respectively. The developed model combined with Monte Carlo simulations can be used as a tool for assessing methane emissions and current inventories.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.222
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2023
Admission routes3
Has abstractyes

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