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Record W4307899046 · doi:10.1016/j.slast.2022.10.001

Digital microfluidics as an emerging tool for bacterial protocols

2022· review· en· W4307899046 on OpenAlex

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

VenueSLAS TECHNOLOGY · 2022
Typereview
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Toronto
FundersSociety for Laboratory Automation and Screening
KeywordsMicrofluidicsInstrumentation (computer programming)Computer scienceDigital microfluidicsNanotechnologySample (material)Systems engineeringBiochemical engineeringComputer hardwareProcess engineeringEngineeringMaterials scienceChromatographyChemistry

Abstract

fetched live from OpenAlex

Bacteria are widely studied in various research areas, including synthetic biology, sequencing and diagnostic testing. Protocols involving bacteria are often multistep, cumbersome and require access to a long list of instruments to perform experiments. In order to streamline these processes, the fluid handling technique digital microfluidics (DMF) has provided a miniaturized platform to perform various steps of bacterial protocols from sample preparation to analysis. DMF devices can be paired/interfaced with instrumentation such as microscopes, plate readers, and incubators, demonstrating their versatility with existing research tools. Alternatively, DMF instruments can be integrated into all-in-one packages with on-chip magnetic separation for sample preparation, heating/cooling modules to perform assay steps and cameras for absorbance and/or fluorescence measurements. This perspective outlines the beneficial features DMF offers to bacterial protocols, highlights limitations of current work and proposes future directions for this tool's expansion in the field.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.321
Teacher spread0.292 · 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