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Record W1682622845 · doi:10.3233/bir-2009-0538

Rethinking turbulence in blood

2009· article· en· W1682622845 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

VenueBiorheology · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTurbulenceMechanicsEddyReynolds numberKolmogorov microscalesLaminar flowPhysicsClassical mechanicsVortexK-epsilon turbulence modelK-omega turbulence model

Abstract

fetched live from OpenAlex

Blood flow, normally laminar, can exhibit high frequency fluctuations suggesting turbulence, which has important implications for the pathophysiology of vascular diseases and the design of blood-bearing devices. According to the classical model of turbulence in a homogeneous fluid, these fluctuations can be attributed to the cascade of eddies down to the Kolmogorov length scale, which, for apparent turbulence in blood, is reported to be on the order of tens of microns. On the other hand, blood is a suspension of mostly red blood cells (RBC), the size and concentration of which would seem to preclude the formation of eddies down to these scales. Assuming dissipation occurs instead via cell-cell interactions mediated by the plasma, here we show how turbulent velocity fluctuations, normally ascribed to turbulent (Reynolds) stresses, could give rise to viscous shear stresses. This may help to resolve fundamental inconsistencies in the understanding of mechanical hemolysis, and it provides a physical basis for the forces actually experienced by formed elements in the blood under nominally turbulent flow. In summary, RBC must be acknowledged as equal players if a satisfactory definition of turbulence in blood is to be achieved.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.163

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 science0.0000.000
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 head0.024
GPT teacher head0.256
Teacher spread0.233 · 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