Image-based tracking of mixed-sized surface sands and gravels under ultraviolet lights in a shallow flume
Bibliographic record
Abstract
Abstract. Simultaneous characterization of the size and organization of both static and moving sediment particles would help to better understand bedform development in river channels. In the current study, an image-based particle tracking method was developed to measure the pathways of sediment particles in transport and visualize their interactions with evolving sedimentary bedforms in flume experiments. The method is novel because it: i) uses ultraviolet lights, fluorescent paint and image segmentation to obtain size class-specific videos of sediment transport over a mixed bed; ii) applies a blob-detection method included in a standalone software (TracTrac – Heyman, 2019) to detect and track particles at rest and in motion; and iii) includes a custom post-processing algorithm that includes a grid-based probabilistic motion model to minimize error in the inferred connections between particle positions on a path. We applied the algorithms on a set of videos taken during a laboratory experiment in which a pair of alternate bars and a cross-over central bar were forming in a shallow flume with non-cohesive sand and gravel transport. When the method works well, as it did for the case of particles in the 2.4–4.0 mm size class (~7 pixels in nominal diameter), the method resulted in an error of +7 % for the number of tracks and -20 % for the average duration of tracks. This degree of accuracy allowed us to analyse the locations of static particles and sediment pathways to show how the active sediment corridor shifted across the frame and then changed angles from the initial trajectories as the set of bars developed. Success of the method is reliant on accurate detection of tracer positions and the ability to predict connections between two identified positions with a motion model. Recommendations are given for application of the method and further testing to reduce reliance on subjective parameters.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".