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A Method for Detecting Chaos in Canine Myocardial Microcirculatory Red Cell Flux

2000· article· en· W2152247150 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

VenueMicrocirculation · 2000
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of New BrunswickQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsCHAOS (operating system)CardiologyFlux (metallurgy)MedicineInternal medicineComputer scienceChemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether red cell movement, as measured by laser Doppler velocimetry, in the capillary net of the beating heart is chaotic. METHODS: Using two dog hearts, in situ red blood cell flux was measured at many sites. Simultaneously, epicardial arterial flow and left ventricular pressure were recorded via transit-time flowmeter and catheter manometer, respectively. The presence or absence of chaos was tested by two methods: Lyapunov exponents and correlation dimension. RESULTS: For capillary red cell flux, the Lyapunov was strongly positive at most sites. It was less so for coronary arterial flow and least for left ventricular pressure. Correlation dimension calculation was less able to distinguish the presence or absence of chaos in capillary red cell tissue flux, coronary arterial flow, and left ventricular pressure. CONCLUSIONS: Capillary red cell flux (movement of red cells in capillaries) is nonlinear, (i.e., chaotic). This complexity suggests that the primary control for oxygen delivery to cardiac myocytes by red blood cells resides in the microcirculation. Also, capillary red cell flux is bifractal, suggesting an ordering of control.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.692

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 head0.016
GPT teacher head0.262
Teacher spread0.245 · 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