A high-performance ray tracing particle tracking model for the simulation of microplastics in inland and coastal aquatic environments
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we present a high-performance Particle Tracking Model (PTM) designed for simulating any type of particles, with a focus on microplastics. The PTM is efficient compared to existing models, parallelized, and utilizes a ray tracing algorithm incorporating both ray reflection and ray refraction in order to traverse particles as well as find the location of each particle over three-dimensional unstructured grids. Various numerical corrections are implemented in the model to address computational round-off errors and discontinuities in the water surface level of the input hydrodynamic models. To increase the accuracy of the model, partially reflective boundary conditions are imposed as well as the capability to simulate microplastics beaching and washout in very shallow areas or dry computational cells. Several tests are conducted to study the performance, scalability, and accuracy of the model. The proposed model is tested with over 3.88 billion double-precision particles on three-dimensional computational grids with up to approximately one million cells. The tests show that the ray tracing approach is efficient, achieves over 17× faster runtime, and offers greater accuracy compared to using an auxiliary grid for particle location finding. For larger timesteps, the ray tracing PTM with refraction shows improved accuracy compared to the ray tracing PTM without refraction. The model's capabilities are tested in a real-world case study over the Saguenay Fjord, Quebec, Canada. The model is utilized to reproduce the paths of five surface drifters. A second numerical test is conducted in the Fjord and high particle concentration areas are identified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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