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Record W2648662755 · doi:10.1109/ccece.2017.7946805

Manifold 2.0: A hardware description language for microfluidic devices

2017· article· en· W2648662755 on OpenAlexaff
Nicholas Klassen, Michael J. Lyons, Michael Prysiazny, Paul F. Roth, Peter Socha, Murphy Berzish, Atulan Zaman, Derek Rayside

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModeling and Simulation Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceProgramming languageDomain-specific languageMicrofluidicsDomain (mathematical analysis)Computer architectureHardware description languageUSableVariety (cybernetics)Class (philosophy)SyntaxComputer hardwareArtificial intelligenceField-programmable gate array

Abstract

fetched live from OpenAlex

Manifold is a generic high-level system design language designed to resemble modern functional programming languages. It is intended to be usable in a variety of design domains that can be conceptualized with components, connectors, ports, and constraints. Domain-specific backends exist for microfluidic devices and digital logic circuits. In Manifold 2.0 we have enhanced both the frontend language and the microfluidic backend. The syntax of the frontend language has been expanded with several useful features, including a type system, a module system, and tuples as first-class values. The microfluidic backend has been extended to generate Modelica code, which can be used to run time-domain simulations in third-party tools such as MapleSim.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.008

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 head0.062
GPT teacher head0.306
Teacher spread0.244 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2017
Admission routes1
Has abstractyes

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