MétaCan
Menu
Back to cohort
Record W2007323932 · doi:10.1109/cicc.2010.5617606

192-channel CMOS neurochemical microarray

2010· article· en· W2007323932 on OpenAlexaff
Meisam Honarvar Nazari, Hamed Mazhab-Jafari, Lian Leng, Axel Güenther, Roman Genov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeurochemicalMillisecondDynamic rangeChannel (broadcasting)MicroarrayChipNeuroscienceComputer scienceMaterials scienceElectronic engineeringChemistryPhysicsPsychologyEngineeringGene expressionTelecommunications

Abstract

fetched live from OpenAlex

A 16×12-channel neurochemical microarray is presented. Each channel acquires bidirectional currents down to pico-amperes proportional to the concentration of a neurochemical. By combining the current-to-frequency and the single-slope analog-to-digital converter (ADC) 110dB of dynamic range is achieved. The ADC in each channel generates a 16-bit output in less than a millisecond. The microarray with flat and 3D gold electrodes and an on-chip microfluidic network is experimentally validated in in-situ recording of neurotransmitter dopamine.

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)0.000
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: none
Teacher disagreement score0.023
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0230.010

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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicNeuroscience and Neural EngineeringFrench-language works237,207