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Record W2797878385 · doi:10.1109/jsen.2018.2825339

Risk Assessment of a Multiplexed Carbon Nanotube Network Biosensor

2018· article· en· W2797878385 on OpenAlexafffund
K. Krishna Mohan, K. Prashanthi, Richard W. Hull, Carlo Montemagno

Bibliographic record

VenueIEEE Sensors Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsAlberta Glycomics CentreUniversity of Alberta
FundersAlberta Innovates - Technology Futures
KeywordsBiosensorAnalytic hierarchy processCarbon nanotubeComputer scienceAnalyteIntuitionMultiplexingNanotechnologyMaterials scienceEngineeringChemistryChromatographyOperations researchTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we report on the fabrication and performance analysis of multiplexed carbon nanotube (CNT) network-based biosensors for detecting real-time deoxyribonucleic acid (DNA) hybridization. We have presented a tailor-made application of analytic hierarchy process (AHP) that matches the risk assessment of such CNT network biosensors. In this respect, we are introducing AHP in the biosensor area for the first time predicting at risk severity assessment for various analyte (DNA) concentrations. Most importantly, we are presenting AHP that allows for an analysis based on multi-factors (multi-criteria) which is augmented intuition along with real-time experimental data and expert proficiency.

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)0.001
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 score0.036
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.293
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations22
Published2018
Admission routes2
Has abstractyes

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