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.
Bibliographic record
Abstract
In cardiovascular flows, Lagrangian coherent structures have been used to explore the skeleton of blood transport. Revealing these transport barriers is instrumental to quantify the mixing and stagnation of blood as well as to highlight locations of elevated strain rate on blood elements. Nevertheless, the clinical use of Lagrangian coherent structures in cardiovascular flows is rarely reported due largely to its non-intuitive nature and computational expense. Here, we explore a recently developed approach called “Lagrangian descriptors,” which quantifies the finite time Euclidean arc length of Lagrangian trajectories released from a grid of initial positions. Moreover, the finite time arc lengths of a set of trajectories capture signatures of Lagrangian coherent structures computed from the same initial condition. Remarkably, the Lagrangian descriptors approach has the most rapid computational performance among all its Lagrangian counterparts. In this work, we explore the application of Lagrangian descriptors for the first time in cardiovascular flows. For this purpose, we consider two in vitro flow models studied previously by our group: flow in an abdominal aortic aneurysm and that in a healthy left ventricle. In particular, we will demonstrate the ability of the Lagrangian descriptors approach to reveal Lagrangian coherent structures computed via the classical geometrical approach, though at a significantly reduced computational cost.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it