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Record W4239747056 · doi:10.1109/tnb.2020.3043557

IEEE Transactions on NanoBioscience publication information

2020· article· en· W4239747056 on OpenAlexaff
Shankar Subramaniam, Ning Xi, Yonhua Tzeng, Jennifer S. Andrew, Ellis Meng, Deirdre Meldrum, Jin-Woo Kim, John T. W. Yeow, Kara Mcarthur, Amir Amani, Frances S. Ligler, Luke P. Lee, Steve Michnik, Yi Pan, Gordon G. Wallace, Lorena Betacour, Shan He, Parag Katira, Valéria Loscrì, Jin Zhang, Toshio Fukuda, Susan Kathy, Land, Kathleen Kramer, Joseph Lillie, M.F.S.F. de Moura, Stephen Phillips, Kamal Sarkar, Kukjin Chun, Robert Fish, Kazuhiro Kosuge, James Conrad, Ljiliana Trajkovic, Stephen Welby, Thomas Siegert, Business Administration, Julie Cozin, Corporate Governance, Donna Hourican, Jamie Moesch, Sophia Muirhead, Lauren Bell, Chris Brantley, Ieee-Usa Cherif, Karen Hawkins, Cecelia Jankowski, Geographic Activities, Steven Heffner, Konstantinos Karachalios, Standards Association, Mary Ward-Callan, Dawn Melley, Kevin Lisankie, Peter Tuohy, Jeffrey Cichocki, Neelam Khinvasara, Patrick Kempf

Bibliographic record

VenueIEEE Transactions on NanoBioscience · 2020
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsCanadian Standards AssociationWestern UniversityMcGill University
Fundersnot available
KeywordsComputer scienceData scienceWorld Wide WebInformation retrieval

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.618
Threshold uncertainty score0.882

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3820.230

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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractno

Explore more

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