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Record W3125178166 · doi:10.1021/cen-09412-govcon004

U.S., Canada commit to methane reductions from oil and gas production

2016· article· en· W3125178166 on OpenAlexaboutno aff
special to C EN Jeff Johnson

Bibliographic record

VenueC&EN Global Enterprise · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCommitMethaneEnvironmental scienceProduction (economics)Fossil fuelMethane emissionsPetroleum engineeringWaste managementChemistryEconomicsGeologyEngineeringComputer scienceOrganic chemistryDatabase

Abstract

fetched live from OpenAlex

U.S. President Barack Obama and Canadian Prime Minister Justin Trudeau earlier this month jointly committed their nations to reduce methane emissions from existing oil and natural gas sources by 40 to 45% from 2012 levels by 2025. Their agreement notes that the oil and gas sector is the world’s largest source of industrial emissions of methane, a potent greenhouse gas. The Environmental Defense Fund, an activist group, says the oil and gas industry is responsible for 90% of U.S. methane emissions. Obama says EPA will begin developing regulations and start a formal process to require companies to provide data on methane emissions. Canada will propose rules by early 2017. In the past, EPA proposed to limit methane emissions from various sources, including oil and gas production facilities, but ran into industry opposition. The American Petroleum Institute says regulations would curb the current U.S. oil and gas bonanza, adding that the

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0100.002
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0260.006

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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2016
Admission routes1
Has abstractyes

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