MétaCan
Menu
Back to cohort
Record W167706634 · doi:10.1007/0-306-48628-8_6

Bioinspired Approaches to Building Nanoscale Devices

2006· book-chapter· en· W167706634 on OpenAlexaff
Sawitri Mardyani, Wen Jiang, Jonathan Lai, Jane Zhang, Warren C. W. Chan

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2006
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanotechnologyNanostructureNanoscopic scaleBiomoleculeBiosensorMaterials scienceComputer science

Abstract

fetched live from OpenAlex

The field of nanotechnology has great potential to change the world. There has been a tremendous focus in the last thirty years on developing and characterizing nanostructure materials. Nowadays, the goal is to utilize these materials as precursors to build nanoscale devices and to develop novel approaches to assemble these precursor nanostructurs into a functional device. Biology offers an excellent guide for assembling nanostructures since a cell can produce thousands of different functional units with only 20-different amino acid building blocks. Biomolecules such as proteins, oligonucleotides and microbial systems have been successfully applied toward organizing nanostructures into macrostructures. Although we have not built complex and functional nanostructures, there are numerous examples in the literature that demonstrate the utility of simple monofunctional nanostructures for biosensing and imaging applications, and drug storage/release systems. In the future, the ability to assemble nanostructures into complex functional units should produce novel systems that will have a broad and significant impact.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.257
Teacher spread0.209 · 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 teacher head, 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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueKluwer Academic Publishers eBooksSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207