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Record W2756223094 · doi:10.1002/2017gl074702

Quantifying CO<sub>2</sub> Emissions From Individual Power Plants From Space

2017· article· en· W2756223094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of TorontoUniversity of WaterlooEnvironment and Climate Change Canada
FundersCalifornia Institute of TechnologyU.S. Environmental Protection AgencyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsEnvironmental scienceObservatoryGreenhouse gasSatelliteEmission inventoryPlumeMeteorologyAtmospheric sciencesRemote sensingAerospace engineeringGeographyGeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In order to better manage anthropogenic CO 2 emissions, improved methods of quantifying emissions are needed at all spatial scales from the national level down to the facility level. Although the Orbiting Carbon Observatory 2 (OCO‐2) satellite was not designed for monitoring power plant emissions, we show that in some cases, CO 2 observations from OCO‐2 can be used to quantify daily CO 2 emissions from individual middle‐ to large‐sized coal power plants by fitting the data to plume model simulations. Emission estimates for U.S. power plants are within 1–17% of reported daily emission values, enabling application of the approach to international sites that lack detailed emission information. This affirms that a constellation of future CO 2 imaging satellites, optimized for point sources, could monitor emissions from individual power plants to support the implementation of climate policies.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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 head0.047
GPT teacher head0.312
Teacher spread0.265 · 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