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Record W2517230367 · doi:10.1039/c6an01781a

Optical disc technology-enabled analytical devices: from hardware modification to digitized molecular detection

2016· article· en· W2517230367 on OpenAlexaff
Samuel S. H. Weng, Xiaochun Li, Yunchao Li, Hua‐Zhong Yu

Bibliographic record

VenueThe Analyst · 2016
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsBurnaby HospitalSimon Fraser University
Fundersnot available
KeywordsBiochipOptical discMicrofluidicsComputer scienceReading (process)Computer hardwareSubstrate (aquarium)SoftwarePoint of carePoint (geometry)NanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Beyond their essential applications in portable data storage for the past 30 years, optical discs and corresponding recording/reading technologies have been extensively explored with the ultimate goal of creating novel analytical tools for on-site chemical analysis and point-of-care (POC) medical diagnosis. In particular, the disc media (CD, DVD, and BD) are proven to be inexpensive and versatile substrate materials for the preparation of various biochips and microfluidic systems; conventional computer drives and disc players are widely adapted for biochip signal reading and microscopic imaging. Herein we provide an overview of such optical disc technology-enabled analytical devices, e.g., integrated systems developed from specifically fabricated analog disks, modified optical drives, or adapted software algorithms.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.008
GPT teacher head0.220
Teacher spread0.211 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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