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Record W2162351861 · doi:10.1109/tnano.2010.2041466

Ion Channel Biosensors—Part II: Dynamic Modeling, Analysis, and Statistical Signal Processing

2010· article· en· W2162351861 on OpenAlex
Vikram Krishnamurthy, Sahar Moradi Monfared, Bruce Cornell

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

VenueIEEE Transactions on Nanotechnology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnalyteBiosensorBiological systemSignal processingNonlinear systemPartial differential equationMolecular biophysicsComputer scienceChemistryPhysicsMaterials scienceMathematicsNanotechnologyChromatographyMathematical analysisTelecommunications

Abstract

fetched live from OpenAlex

This paper deals with the dynamic modeling, analysis, and statistical signal processing of the ion channel switch biosensor. The electrical dynamics are described by a second-order linear system. The chemical kinetics of the biosensor response to analyte concentration in the reaction-rate-limited regime are modeled by a two-timescale nonlinear system of differential equations. Also, the analyte concentration in the mass-transport-influenced regime is modeled by a partial differential equation subject to a mixture of Neumann and Dirichlet boundary conditions. By using the theory of singular perturbation, we analyze the model so as to predict the performance of the biosensor in transient and steady-state regimes. Finally, we outline the use of statistical signal processing algorithms that exploit the biosensor dynamics to classify analyte concentration.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.663

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 head0.007
GPT teacher head0.239
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