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Record W2094651612 · doi:10.4155/fmc.10.240

Research Spotlight: Bionanotechnology: Small can Have a Big Impact in the Medical Sciences: A Win-Win Situation. Part 1.

2010· article· en· W2094651612 on OpenAlexafffundabout
John F. Honek, Alain Francq, Arthur J. Carty

Bibliographic record

VenueFuture Medicinal Chemistry · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersCanada Research Chairs
KeywordsNanobiotechnologyNanotechnologyNanomedicineEngineeringComputer scienceMaterials scienceNanoparticle

Abstract

fetched live from OpenAlex

Bionanotechnology blends the areas of nanotechnology and biological sciences. It focuses on a wide area of fundamental research and engineering such as the fabrication of nanomaterials utilizing components and catalysts from the biological sciences as well as the application of nanotechnology to understanding and quantifying biological systems. Areas such as the development of nanomaterial-based drug-delivery systems, the development of novel biosensors and their application to clinical diagnostics as well as high-throughput screening and the exploration of how nanomaterials interact with living systems are all subjects of intense current interest. The Waterloo Institute for Nanotechnology (WIN), coordinates various leading-edge nanotechnology research areas at the University of Waterloo (Canada). One focus is the area of bionanotechnology.

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)0.003
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.046
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0090.004
Insufficient payload (model declined to judge)0.0460.023

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 head0.032
GPT teacher head0.352
Teacher spread0.320 · 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

Citations2
Published2010
Admission routes3
Has abstractyes

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