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Record W2117552063 · doi:10.1126/science.1154533

Assembling Materials with DNA as the Guide

2008· review· en· W2117552063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience · 2008
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill UniversityCanadian Institute for Advanced Research
Fundersnot available
KeywordsTemplateNanotechnologyDNA nanotechnologyDNANanostructureDNA origamiGenomicsComputer scienceComputational biologyMaterials scienceGenomeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

DNA's remarkable molecular recognition properties and structural features make it one of the most promising templates to pattern materials with nanoscale precision. The emerging field of DNA nanotechnology strips this molecule from any preconceived biological role and exploits its simple code to generate addressable nanostructures in one, two, and three dimensions. These structures have been used to precisely position proteins, nanoparticles, transition metals, and other functional components into deliberately designed patterns. They can also act as templates for the growth of nanowires, aid in the structural determination of proteins, and provide new platforms for genomics applications. The field of DNA nanotechnology is growing in a number of directions, carrying with it the promise to substantially affect materials science and biology.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.362
Teacher spread0.336 · 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