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Record W4402532206 · doi:10.1016/j.egycc.2024.100154

Carbon management technology pathways for reaching a U.S. Economy-Wide net-Zero emissions goal

2024· article· en· W4402532206 on OpenAlexfundno aff
Matthew Binsted, Ellie Lochner, Jae Edmonds, José Morales Benitez, John Bistline, Morgan Browning, Francisco C. de la Chesnaye, Jay Fuhrman, Leonard Göke, Gokul Iyer, Kathleen M. Kennedy, Page Kyle, Carol S. Lenox, Haewon McJeon, Kowan T.V. O'Keefe, Patrick O’Rourke, Amogh Prabhu, Ronald D. Sands, Luis Sarmiento, Sharon Showalter, Frances Wood, Sha Yu

Bibliographic record

VenueEnergy and Climate Change · 2024
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryEuropean Research CouncilSocial Sciences and Humanities Research Council of CanadaU.S. Department of EnergyEuropean CommissionNational Energy Technology LaboratoryOffice of Fossil Energy and Carbon ManagementHorizon 2020 Framework ProgrammeOffice of Fossil EnergyHorizon 2020Technische Universität BerlinU.S. Environmental Protection AgencyElectric Power Research InstitutePierre Elliott Trudeau FoundationEconomic Research ServiceU.S. Department of Agriculture
KeywordsCarbon fibersZero emissionZero (linguistics)Environmental scienceNatural resource economicsEconomicsEngineeringWaste managementComputer science

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.210
Teacher spread0.194 · 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 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

Citations17
Published2024
Admission routes1
Has abstractyes

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