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Record W4247231872 · doi:10.1002/celc.201300258

Tailoring Biomass‐Derived Carbon Nanoarchitectures for High‐Performance Supercapacitors

2014· article· en· W4247231872 on OpenAlexafffundabout
Huanlei Wang, Zhi Li, David Mitlin

Bibliographic record

VenueChemElectroChem · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Research Council CanadaUniversity of Alberta
FundersUniversity of Alberta
KeywordsSupercapacitorBiomass (ecology)GrapheneNanotechnologyElectrochemical energy storageMaterials scienceSustainabilityCarbon fibersCover (algebra)Energy storageElectrochemistryEnvironmental scienceProcess engineeringWaste managementPulp and paper industryEngineeringMechanical engineeringChemistryPhysicsComposite materialComposite numberPower (physics)ElectrodeGeologyEcology

Abstract

fetched live from OpenAlex

Abstract Invited for this month’s cover is Prof. Dave Mitlin′s group at the University of Alberta and NINT NRC. The image highlights the amazing potential for sustainably transforming a range of “waste” biomass precursors into truly value‐added energy‐storage carbons, some being akin to graphene in both structure and electrochemical properties. Read the full text of the article at 10.1002/celc.201300127 .

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.946

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2014
Admission routes3
Has abstractyes

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