Validation of model-based correction for non-Stokesian tracers
Bibliographic record
Abstract
In-situ flow-tracking measurements at scales on the order of 10 m3 and larger remain a challenge. The large size of the tracers required for optical visibility results in an inertial lag and inherently low seeding density. For instance, natural snowfall, fake snow and soap bubbles on the order of 2 cm have been used as tracers for field measurements and extracted statistical quantities (Nemes et al., 2017; Wei et al., 2021; Rosi et al., 2014). There is also growing interest in networks of sensors for remote- measurement where optical access is impossible (Bolt et al. 2020; Villa et al. 2016). Onboard inertial measurement units (IMU) are a promising tool for high-resolution measurements over large spatial domains without optical access. However, due to the intrinsic lag, a dynamic-model-based correction is required for the tracking of transient phenomena, sketched in figure 1. In the present study, the tracer-velocity correction is evaluated by quantifying the residual error in measured flow velocity after the method of Galler et al. (2021) is applied.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".