Propagating Particle Tracking Uncertainty Defined by Fuzzy Numbers in Spatially Variable Velocity Fields
Bibliographic record
Abstract
Accurate prediction of the trajectories of material drifting on the ocean surface is critical for risk assessment and responses to environmental emergencies. Prediction of these trajectories is subject to uncertainty arising from a number of sources, with a primary source being uncertainty in the modelled ocean surface currents and winds used as input to the trajectory model. This article presents a fuzzy number-based algorithm for propagating uncertainty through a particle tracking scheme in a time- and space-varying velocity field. The performance of the algorithm was tested by applying it to idealized, analytical velocity fields and scoring the results against the analytical solution. Both epistemic and aleatoric uncertainty were considered and combined using a fractional Brownian motion model for temporal autocorrelation of the uncertainty. In the evaluation of the algorithm, sensitivity was quantified with respect to parameters such as timestep size, resolution of the forcing velocity field, spatial and temporal gradients in the forcing, and resolution of the applied uncertainty. Parameter values optimizing uncertainty representation and computational cost were identified. The applied uncertainty was found to evolve in agreement with classical relative dispersion relationships.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".