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Record W3012193018 · doi:10.1038/s41565-020-0656-y

Banning carbon nanotubes would be scientifically unjustified and damaging to innovation

2020· letter· en· W3012193018 on OpenAlexafffund
Daniel A. Heller, Prakrit V. Jena, Matteo Pasquali, Kostas Kostarelos, Lucia Gemma Delogu, Rachel E. Meidl, Slava V. Rotkin, David A. Scheinberg, Robert E. Schwartz, Mauricio Terrones, YuHuang Wang, Alberto Bianco, Ardemis A. Boghossian, Sofie Cambré, Laurent Cognet, Simon R. Corrie, Philip Demokritou, Silvia Giordani, Tobias Hertel, Tetyana Ignatova, Mohammad F. Islam, Nicole M. Iverson, Anand Jagota, Dawid Janas, Junichiro Kono, Sebastian Kruss, Markita P. Landry, Yan Li, Richard Martel, Shigeo Maruyama, Anton V. Naumov, Maurizio Prato, Susan J. Quinn, Daniel Roxbury, Michael S. Strano, James M. Tour, R. Bruce Weisman, Wim Wenseleers, Masako Yudasaka

Bibliographic record

VenueNature Nanotechnology · 2020
Typeletter
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de Montréal
FundersNational Eye InstituteWeill Cornell Medical CollegePeking UniversityInstitut Català de Nanociència i NanotecnologiaSilesian University of TechnologyUniversiteit AntwerpenCentre National de la Recherche ScientifiqueUniversité de MontréalMemorial Sloan-Kettering Cancer CenterLehigh UniversityUniversità degli Studi di PadovaUniversité de BordeauxNational Institute of General Medical SciencesMonash UniversityGeorg-August-Universität GöttingenDublin City UniversityUniversity of ManchesterNational Institute of Advanced Industrial Science and TechnologyTexas Christian UniversityUniversity of Nebraska-LincolnNational Institute on Drug AbuseUniversity of North Carolina at GreensboroUniversité de StrasbourgRice UniversityUniversity of TokyoPennsylvania State UniversityCarnegie Mellon UniversityMassachusetts Institute of Technology
KeywordsCarbon nanotubeNanotechnologyCarbon fibersMaterials scienceComposite material

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.020
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: Commentary
Teacher disagreement score0.093
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0070.007
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0930.040
Insufficient payload (model declined to judge)0.0070.004

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Citations104
Published2020
Admission routes2
Has abstractno

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