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Record W4210954067 · doi:10.1088/1361-6439/ac545f

A perspective of active microfluidic platforms as an enabling tool for applications in other fields

2022· article· en· W4210954067 on OpenAlex
Marie Hébert, Jan P. Huissoon, Carolyn L. Ren

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsModular designNanotechnologyDigital microfluidicsComputer scienceMicrofluidic chipLeverage (statistics)EngineeringElectrowettingMaterials scienceArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Microfluidics has progressed tremendously as a field over the last two decades. Various areas of microfluidics developed in fully-fledged domains of their own such as organ-on-a-chip, digital and paper microfluidics. Nevertheless, the technological advancement of microfluidics as a field has not yet reached end-users for independent use. This is the key objective that is kept as a lens throughout this review. The ultimate goal is for microfluidics to be simply considered as a tool for application-focused research. A modular automated platform is envisioned to provide the stacking and modularity required to lower the knowledge barrier for end-users. The literature considered in this review is limited to active microfluidics and the analysis focuses on the potential for end-users to independently leverage the platforms for research in various fields such as cell assays, biochemistry, materials, and environmental factors monitoring.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.607

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 head0.006
GPT teacher head0.221
Teacher spread0.214 · 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