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Record W4250613527 · doi:10.1007/978-90-481-9751-4_205

Nanorobotics for Bioengineering

2012· book-chapter· en· W4250613527 on OpenAlexaff
Minami Yoda, Jean‐Luc Garden, Olivier Bourgeois, Aeraj Haque, Aloke Kumar, Hans Deyhle, Simone Hieber, Bert Müller, Mary Cano‐Sarabia, Daniel Maspoch, Константин Соболев, Florence Sanchez, Esmaiel Jabbari, J. Tanner Nevill, Daniele Malleo, Peter Bøggild, Wei Chen, Chunlei Wang, Bharat Bhushan, Manuel L. B. Palacio, Shrikant C. Nagpure, Mónica Lira‐Cantú, Irene González‐Valls, Rustom B. Bhiladvala, Nastassja A. Lewinski, Matthew Wright, Paola Martino, Paolo Allia, Alessandro Chiolerio, Jason P. Gleghorn, Celeste M. Nelson, Emiliano Descrovi, Mirko Ballarini, Francesca Frascella, Daniel Neuhauser, Christopher Arntsen, Kenneth A. Lopata, Lixin Dong, Xinyong Tao, Zheng Fan, Li Zhang, Xiaobin Zhang, Bradley J. Nelson, Soichiro Tsuda, Sylvain Martel, Didi Xu, Timothy J. Merkel, Joseph M. DeSimone, Lucio Colombi Ciacchi, Susan Köppen, Michael Nosonovsky, Dongchan Jang, Jason Li, Steve To, Lidan You, Yu Sun, Lorenzo Lunelli, Cristina Potrich, Laura Pasquardini, Cecilia Pederzolli, Mariangela Lombardi, Menghan Zhou, Jian He, Luca Boarino, Giampiero Amato, Ille C. Gebeshuber, David W. Lee, S. Bianco, Angelica Chiodoni, Claudio Gerbaldi, Marzia Quaglio, Andréa Toma, Remo Proietti Zaccaria, Roman Krahne, Alessandro Alabastri, Maria Laura Coluccio, Gobind Das, Carlo Liberale, Francesco De Angelis, Marco Francardi, Federico Mecarini, Francesco Gentile, Angelo Accardo, Liberato Manna, Enzo Di Fabrizio, Paola Rivolo, Kuo-Sheng Ma, Lu Dai, Yongfen Qi, Lixin Jia, Wei Yu, Li‐Lin Du, Satish C. Chaparala, Vikram Bhatia, Nipun Sinha, Matteo Rinaldi, Ramakrishna Podila, Benoy Anand, S. Siva Sankara Sai, K. Venkataramaniah, Reji Philip, Apparao M. Rao

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNanoroboticsComputer scienceArtificial intelligence

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0410.020

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 head0.022
GPT teacher head0.230
Teacher spread0.208 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractno

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