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Record W2889424096 · doi:10.1126/science.aar6859

Global carbon intensity of crude oil production

2018· article· en· W2889424096 on OpenAlexfundno aff
Mohammad S. Masnadi, Hassan M. El-Houjeiri, Dominik Schunack, Yunpo Li, Jacob Englander, Alhassan Badahdah, Jean‐Christophe Monfort, James E. Anderson, Timothy J. Wallington, Joule Bergerson, Deborah Gordon, Jonathan Koomey, Steven Przesmitzki, Inês L. Azevedo, Xiaotao Bi, J. Emmett Duffy, Garvin Heath, Gregory A. Keoleian, Christophe McGlade, D. Nathan Meehan, Sonia Yeh, Fengqi You, Adam R. Brandt

Bibliographic record

VenueScience · 2018
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Energy Technology LaboratoryWilliam and Flora Hewlett FoundationFord Motor CompanyAlfred P. Sloan Foundation
KeywordsGreenhouse gasProduction (economics)Crude oilEnvironmental scienceIntensity (physics)Natural resource economicsCarbon fibersBusinessAgricultural economicsEconomicsPetroleum engineeringComputer scienceEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

New data enable targeted policy to lessen GHG 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)0.001
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.298
Teacher spread0.283 · 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

Citations340
Published2018
Admission routes1
Has abstractyes

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