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Record W4385586895 · doi:10.17756/nwj.2023-suppl1

9th Nanotech & Nanomaterials Research Conference (Nano Rome 2023)

2023· article· en· W4385586895 on OpenAlexaff
Liqiu Wang, Shinji Takeoka, Todd D. Giorgio, Sydney Henriques, Ori Z. Chalom, Evan B. Glass, Sohini Roy, Abigail Man- Ning, Laura C. Kennedy, Young Jae Kim, Fiona E. Yull, Yiyuan Zhang, Zhandong Huang, Zheren Cai, Yuqing Ye, Zheng Li, Feifei Qin, Junfeng Xiao, Dongxing Zhang, Qiuquan Guo, Yanlin Song, Jun Yang, Naoki Komatsu, Jason Thomas Duskey, Ilaria Ottonelli, Giovanni Tosi, Barbara Ruozi, Maria Angela Vandelli, Annj Zamuner, Elena Zeni, Leonardo Cassari, Giovanna Iucci, Antonio Gloria, Francesca Ravanetti, An- Tonio Cacchioli, Gabriella D’Auria, Lucia Falcigno, Paola Brun, Monica Dettin, Haider Butt, Muhammrd Hisham, Ahmed E. Salih, Sara Cerra, Tommaso Salamone, Martina Mercurio, Hajareh Farid, Beatrice Haghighi, Carla Pennacchi, Ilaria Sappino

Bibliographic record

VenueNanoWorld Journal · 2023
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsWestern University
FundersColegiul Consultativ pentru Cercetare-Dezvoltare şi InovareUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiMinisterul Cercetării, Inovării şi Digitalizării
KeywordsNanomaterialsNano-NanotechnologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Liqiu Wang Droplets of nano-/subnano-liter are useful in a wide range of applications, particularly when their size is uniform and controllable. Examples include biochemistry, biomedical engineering, food industry, pharmaceuticals, and material sciences.

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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.810
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1900.157

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 head0.090
GPT teacher head0.361
Teacher spread0.270 · 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.

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
Published2023
Admission routes1
Has abstractyes

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