Evaluating microplastic trapping efficiency in seagrass meadows using hydraulic flume simulations
Bibliographic record
Abstract
Microplastic (MP) pollution poses a significant environmental threat, with projections indicating a 50-fold increase in pollution levels by 2100. Seagrass meadows, important for carbon storage and sediment stabilisation, may also serve as a Nature-based Solution for MP pollution. Despite the well-documented presence of MPs in seagrass sediments, the efficiencies of MP capture by these habitats remain largely unexplored. In this study, hydraulic flume simulations were conducted to assess how different seagrass planting configurations influence MP trapping. The results indicate that meadows with random spatial distribution are 6 % more effective at trapping MPs under high concentrations compared to grid-patterned meadows, while lower planting densities enhance trapping efficiency by 14 %. These findings offer insights into optimising seagrass restoration efforts for mitigating MP pollution, and this highlights the need for further needed to understand the broader ecological implications of MP retention in these critical ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".