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Record W3043918843 · doi:10.1007/978-981-15-5712-5_7

Fluid Dynamics in Deformable Microchannels

2020· book-chapter· en· W3043918843 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

VenueMechanical sciences · 2020
Typebook-chapter
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrofluidicsFluid mechanicsComputer scienceRoboticsArtificial intelligenceMechanicsRobotNanotechnologyPhysicsMaterials science

Abstract

fetched live from OpenAlex

The study of deformable channels finds particular interest among the biofluid dynamists as models to physiological vessels, especially arteries. They serve as a convenient laboratory platform in which experiments can be conducted in controlled settings. This is important when the actual tests on living subjects become difficult to perform due to ethical constraints and poor control over multiple experimental and theoretical parameters. Starting from the earliest findings of William Harvey on the circulation of blood, we have evolved a great extent up to solving complex mathematical models pertaining to arterial mechanics using supercomputers. With the advent of rapid prototyping, robotics, image processing, and high-end digital capabilities, detailed investigations can be carried out in a fast and accurate manner with the least human intervention. To this end, microfluidics technology offers a great advantage due to its inherent capabilities of addressing many fundamental issues that affect the biofluid mechanics in physiological conduits. The present chapter deals with these aspects starting with the history of biofluid mechanics to the state of the art of microfluidics addressing it from both theoretical and experimental perspectives.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.910

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 science0.0010.000
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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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