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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 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

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.635

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 communication0.0010.001
Open science0.0010.000
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 head0.062
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

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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