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Record W4252983671 · doi:10.1063/5.0025859.2

10.1063/5.0025859.2

2020· dataset· en· W4252983671 on OpenAlexaff
Maziar Sargordi, Anna Chtchetinina, Giuseppe Di Labbio, Hoi Dick Ng, Lyes Kadem

Bibliographic record

VenueDefault Digital Object Group · 2020
Typedataset
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsConcordia University
Fundersnot available
KeywordsParticle image velocimetryMechanicsFlow (mathematics)PlanarDissipationVentriclePhysicsComputer scienceCardiologyMedicineThermodynamics

Abstract

fetched live from OpenAlex

Edge-to-edge repair is a procedure introduced to overcome mitral valve regurgitation. However, it leads to an unusual flow in the left ventricle characterized by twin parallel pulsed jets. This type of flow has not been extensively investigated in the literature. We set up a basic experiment to better characterize this type of flow from a fundamental point of view. Planar time-resolved particle image velocimetry measurements were performed downstream of three configurations of mitral valves corresponding to healthy and repaired valves. The flow field is characterized using velocity profiles, viscous energy dissipation, and time-frequency spectra, and their potential clinical impact is highlighted.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.744
Threshold uncertainty score0.855

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.2560.531

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 head0.306
Teacher spread0.294 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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