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Record W3121448056 · doi:10.1021/es1013278

Land Use Greenhouse Gas Emissions from Conventional Oil Production and Oil Sands

2010· article· en· W3121448056 on OpenAlexaffabout
Sonia Yeh, Sarah M. Jordaan, Adam R. Brandt, Merritt R. Turetsky, Sabrina Spatari, David W. Keith

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of GuelphUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasEnvironmental scienceOil sandsFossil fuelBiofuelLand reclamationOil refineryRaw materialRefineryEnvironmental engineeringWaste managementGeologyAsphaltChemistryEcologyEngineeringGeography

Abstract

fetched live from OpenAlex

Debates surrounding the greenhouse gas (GHG) emissions from land use of biofuels production have created a need to quantify the relative land use GHG intensity of fossil fuels. When contrasting land use GHG intensity of fossil fuel and biofuel production, it is the energy yield that greatly distinguishes the two. Although emissions released from land disturbed by fossil fuels can be comparable or higher than biofuels, the energy yield of oil production is typically 2-3 orders of magnitude higher, (0.33-2.6, 0.61-1.2, and 2.2 5.1 PJ/ha) for conventional oil production, oil sands surface mining, and in situ production, respectively). We found that land use contributes small portions of GHGs to life cycle emissions of California crude and in situ oil sands production ( <0.4% or < 0.4 gCO₂e/MJ crude refinery feedstock) and small to modest portions for Alberta conventional oil (0.1-4% or 0.1-3.4 gCO₂e/MJ) and surface mining of oil sands (0.9-11% or 0.8-10.2 gCO₂e/MJ).Our estimates are based on assumptions aggregated over large spatial and temporal scales and assuming 100% reclamation. Values on finer spatial and temporal scales that are relevant to policy targets need to account for site-specific information, the baseline natural and anthropogenic disturbance.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.998

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 studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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.

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
Published2010
Admission routes2
Has abstractyes

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