Robust approach to monitoring Lagrangian transport in very large volume
Bibliographic record
Abstract
State-of-the-art flow measurements utilize four or more high-speed cameras to perform highly-accurate Lagrangian particle tracking (LPT) in small to medium-sized measurement volumes (Schanz et al., 2016). Hou et al. (2021) suggested a novel approach to allow measurements in significantly larger measurement volumes (O(10m3 )) while reducing the experimental effort. 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 bubbleimage size. 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 (Kahler et al., 2020). In the context of the recent pandemic, the latter application could ¨help to reduce infection risk by designing appropriate air circulation. Hereby, frequent air exchange is recommended, while direct airflow from individual to individual should be avoided (WHO, 2020). The present study strives to optimize and simplify the experimental set-up as well as to characterize the accuracy of the novel single-camera approach. Figure 1(a) shows the set-up used to characterize the novel approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".