Microparticle dynamics in upper-ocean turbulence: Dataset for analysis, modeling & prediction
Bibliographic record
Abstract
Plastic particle pollution has threatened the well-being of seawater ecosystems over the past decades. Therefore, understanding, modeling and (potentially) predicting the dynamics of microplastics and biogenic particles in ocean turbulence is of utmost importance to help develop mitigation strategies and propose technological solutions ultimately aimed at safeguarding global water systems. This is particularly significant for microplastics in the upper-ocean layer. To that end, this work presents a comprehensive and openly accessible dataset carefully designed to explore the interplay between the flow physics of particle-laden turbulence and the physicochemical effects of biofilm stickiness. The dataset comprises nine point-particle direct numerical simulations of fluid flow featuring microplastic and biogenic debris within a periodic three-dimensional flow domain. In all cases, the chosen turbulent intensity and microparticle properties represent conditions observed in the upper-ocean layer. This data repository aims to facilitate in-depth exploration, modeling and prediction of the intricate flow physics observed in marine microplastics, particularly regarding their distribution and aggregation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".