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Record W1974888008 · doi:10.1021/ac403196b

Visual Detection of DNA on Paper Chips

2014· article· en· W1974888008 on OpenAlex
Yajing Song, Péter Gyarmati, Ana Catarina Araújo, Joakim Lundeberg, Harry Brumer, Patrik L. Ståhl

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

VenueAnalytical Chemistry · 2014
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsChemistryVisualizationNanotechnologyComputational biologyNaked eyeDNANucleic acidFilter (signal processing)Computer scienceArtificial intelligenceDetection limitComputer visionChromatography

Abstract

fetched live from OpenAlex

On-site DNA analysis for diagnostic or forensic purposes is much anticipated in the future of molecular testing. Yet the challenges to achieve this goal remain large with rapid and inexpensive detection and visualization being key factors for any portable analysis system. We have developed a filter paper-based nucleic acid assay, which is able to identify and distinguish dog and human genomic and mitochondrial samples in a forensic setting. The filter paper material allows for transport by capillary force of the sample DNA through the detection surface, allowing the targets to hybridize specifically to their complementary capture sequences. Coupling micrometer-sized beads to DNA allows the results to be visualized by the naked eye, enabling instant, cost-efficient, and on-site detection, while eliminating the need for advanced expensive instrumentation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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