Echo-Lagrangian particle tracking: an ultrasound-based method for extracting path-dependent flow quantities
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
Abstract Eulerian, ultrasound-based velocimetry has become a popular tool for evaluating non-optically accessible flows, and has demonstrated great potential for medical flows. In contrast, the current study presents a Lagrangian method of extracting path-dependent dynamics from time-resolved ultrasound images referred to here as echo-Lagrangian particle tracking (echoLPT). Ultrasound system parameters specific to Lagrangian tracking are detailed for recording pulsatile flow through an idealized stenosis model. Furthermore, seeding materials and image processing procedures are discussed in order to improve signal-to-noise ratio and minimize particle image ambiguity. The pathlines that result from echoLPT reveal mixing downstream of the stenosis, and yield time-resolved, path-dependent information. As a means to demonstrate the value of echoLPT, particle residence time (PRT) in the post-stenotic region is calculated. PRT is the length of time a fluid parcel remains within a region of interest, and is used to highlight the effects of pulsatility. For the pulsatile flows tested, PRT is shown to increase with the frequency of pulsation as fluid is swept into the recirculation region, while PRT is decreased with increasing mean Reynolds number and amplitude ratio.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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