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Record W2153098547 · doi:10.1109/iembs.1995.579805

Electrical coupling in longitudinal colonic smooth muscle

2002· article· en· W2153098547 on OpenAlexaff
Sara Azizi, Berj L. Bardakjian, W.N. Wright, K. Hall, Jan D. Huizinga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsCoupling (piping)Electrical impedanceResistive touchscreenCapacitive sensingSmooth muscleCapacitive couplingWhite noiseFunction (biology)Noise (video)Biological systemMaterials sciencePhysicsComputer scienceBiologyVoltageTelecommunicationsMedicineInternal medicine

Abstract

fetched live from OpenAlex

This study investigates the nature of electrical coupling between cells and estimates the passive electrical parameters of the longitudinal colonic smooth muscle. Gaussian white noise measurements and analysis techniques are used to obtain the input impedance function of a strip of smooth muscle from the dog colon. A system model approach based on a network model is used to investigate different coupling mechanisms. A gradient method of optimization is then used to fit the analytical input impedance of the network model to the measured input impedance. Our results indicate that the longitudinal colonic smooth muscle may be composed of many groups of cells; while cells are tightly coupled within a group, different groups are weakly coupled by resistive and capacitive components.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.205
Teacher spread0.183 · 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 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

Citations0
Published2002
Admission routes1
Has abstractyes

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