Impact of Microplastics on Oil Dispersion Efficiency in the Marine Environment
Bibliographic record
Abstract
Oil spill and microplastics (MPs) pollution has raised global concerns, due to the negative impacts on ocean sustainability. Chemical dispersants were widely adopted as oil-spill-treating agents. When MPs exist during oil dispersion, MP/oil-dispersant agglomerates (MODAs) are observed. This study explored how MPs affect oil-dispersion efficiency in oceans. Results showed that, under dispersant-to-oil volumetric ratio (DOR) 1:10 and mixing energy of 200 rpm, the addition of MPs increased the oil droplet size, total oil volume concentration, and oil-dispersion efficiency. Under DOR 1:25 and mixing energy of 120 rpm, the addition of MPs increased the oil droplet size but resulted in a decrease of total oil volume concentration and dispersion efficiency. Compared with the oil volume concentration, the oil droplet size may no longer be an efficient parameter for evaluating oil-dispersion efficiency with the existence of MODAs. A machine learning (ML)-based XGBRegressor model was further constructed to predict how MPs affected oil volume concentration and oil-dispersion efficiency in oceans. The research outputs would facilitate decision-making during oil-spill responses and build a foundation for the risk assessment of oil and MP co-contaminants that is essential for maintaining ocean sustainability.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".