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Record W1989505181 · doi:10.1073/pnas.0709758104

Universal scaling law of electrical turbulence in the mammalian heart

2007· article· en· W1989505181 on OpenAlexaff
Sami F. Noujaim, Omer Berenfeld, Jérôme Kalifa, Marina Cerrone, Kumaraswamy Nanthakumar, Felipe Atienza, Javier Moreno, Sergey Mironov, José Jalife

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2007
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsToronto General Hospital
FundersNational Heart, Lung, and Blood InstituteAmerican College of Cardiology FoundationAmerican Heart Association
KeywordsScalingExponentScaling lawTurbulenceAllometryVentricular fibrillationPhysicsOptical mappingStatistical physicsBiologyCardiologyMechanicsMathematicsMedicineGeometry

Abstract

fetched live from OpenAlex

Many biological processes, such as metabolic rate and life span, scale with body mass (BM) according to the universal law of allometric scaling: Y = aBM(b) (Y, biological process; b, scaling exponent). We investigated whether the temporal properties of ventricular fibrillation (VF), the major cause of sudden and unexpected cardiac death, scale with BM. By using high-resolution optical mapping, numerical simulations and metaanalysis of VF data in 11 mammalian species, we demonstrate that the interbeat interval of VF scales as VF(cycle) (length) = 53 x BM(1/4), spanning more than four orders of magnitude in BM from mouse to horse.

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)0.005
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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
GenreEmpirical

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

Citations55
Published2007
Admission routes1
Has abstractyes

Explore more

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