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Record W2504836500 · doi:10.3390/bios6030042

Nanopore-CMOS Interfaces for DNA Sequencing

2016· review· en· W2504836500 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiosensors · 2016
Typereview
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsYork University
FundersCMC Microsystems
KeywordsNanoporeInterfacingNanopore sequencingCMOSNanotechnologyContext (archaeology)Computer scienceInterface (matter)DNA sequencingMaterials scienceDNAEngineeringElectronic engineeringChemistryComputer hardwareBiologyParallel computing

Abstract

fetched live from OpenAlex

DNA sequencers based on nanopore sensors present an opportunity for a significant break from the template-based incumbents of the last forty years. Key advantages ushered by nanopore technology include a simplified chemistry and the ability to interface to CMOS technology. The latter opportunity offers substantial promise for improvement in sequencing speed, size and cost. This paper reviews existing and emerging means of interfacing nanopores to CMOS technology with an emphasis on massively-arrayed structures. It presents this in the context of incumbent DNA sequencing techniques, reviews and quantifies nanopore characteristics and models and presents CMOS circuit methods for the amplification of low-current nanopore signals in such interfaces.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.058
GPT teacher head0.291
Teacher spread0.233 · 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