Bibliographic record
Abstract
State-of-the-art flow measurements typically utilize four or more high-speed cameras to perform highly-accurate Lagrangian particle tracking (LPT) over small-to-medium-sized measurement volumes (Schanz et al., 2016). Recently, Hou et al. (2021) proposed a novel LPT approach that allows for measurement over significantly larger measurement volumes on the order of 10m3 while simplifying the experimental setup. Here, a single camera is used to track centimeter-sized soap bubbles in three dimensions by not only evaluating the bubble-center location but also the bubble-image size itself. Possible applications of the suggested approach include – but are not limited to – measurements in industrial wind tunnels (Hou et al., 2021), full-scale measurements in the atmospheric boundary layer (Rosi et al., 2014; Toloui et al., 2014), and the characterization of airflow in indoor spaces, such as offices or classrooms (Kaehler et al., 2020). With such large-scale measurements come challenges associated with identifying characteristic features with inherently sparse data. Existing approaches, including coherent-structure coloring (CSC) – see Schlueter-Kuck Dabiri (2017)) and Martins et al. (2021) – will be reviewed before a new approach based on multi-scale recurrence networks will be tested on a series of canonical problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".