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Record W2299945046 · doi:10.1002/anie.201600678

Cover Picture: Target‐Induced and Equipment‐Free DNA Amplification with a Simple Paper Device (Angew. Chem. Int. Ed. 8/2016)

2016· paratext· en· W2299945046 on OpenAlexaff
Meng Liu, Christy Y. Hui, Qiang Zhang, Jimmy Gu, Balamurali Kannan, Sana Jahanshahi‐Anbuhi, Carlos D. M. Filipe, John D. Brennan, Yingfu Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typeparatext
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDNAPrimer (cosmetics)Simple (philosophy)Applications of PCRPolymerase chain reactionComputational biologyMolecular biologyComputer scienceNanotechnologyChemistryBiologyGeneticsMultiplex polymerase chain reactionMaterials scienceGene

Abstract

fetched live from OpenAlex

A simple paper-based device for DNA amplification is described by J. D. Brennan, Y. Li, and co-workers in their Communication on page 2709 ff. The paper device is printed with all enabling reagents, such as DNA polymerase, circular DNA template, and small building blocks, for building long-chain DNA molecules for visual detection. DNA amplification is automatically activated upon addition of a test sample that contains the DNA or RNA sequence of interest.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.271
Threshold uncertainty score0.907

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2710.112

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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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
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
Published2016
Admission routes1
Has abstractyes

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