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Record W1978237395 · doi:10.1109/robot.2010.5509762

Computer-assisted patch clamping

2010· article· en· W1978237395 on OpenAlexaff
Mahdi Azizian, Rajni V. Patel, Cezar Gavrilovici, Michael O. Poulter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsWestern University
Fundersnot available
KeywordsClampingComputer scienceSoftwarePipetteProcess (computing)Haptic technologyComputer visionRobotTask (project management)Computer hardwareArtificial intelligenceSimulationEngineering

Abstract

fetched live from OpenAlex

Patch clamping is an electrophysiological technique that permits the measurement of ion channel activity in many different kinds of cells. Placement of the patch clamp electrodes using micromanipulators is a time consuming and complicated task due to the lack of depth perception of microscope optics and the constrained physical environment. In order to simplify this process, a software platform has been created that permits the user to easily perform not only single electrode recordings but multiple ones. The software platform provides capabilities for automatic positioning of micropipettes in specified locations on the image plane, autofocusing on selected objects, detecting visible micropipettes using image processing techniques, haptic-enabled master slave control of micromanipulators for accurate positioning of electrodes while generating virtual forces to prevent collision between micropipettes, as well as several other novel features which help the user to perform patch clamping more efficiently. The system does not require any changes in the hardware, and uses a fully software-based approach.

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)0.000
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 score0.025
Threshold uncertainty score0.309

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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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