Why biofouling cannot contribute to the vertical transport of small microplastic
Bibliographic record
Abstract
In contrast to expectations, even buoyant microplastics like polyethylene and polypropylene are found at high concentrations in deep sediment traps and deep-sea sediments. To explain the presence of such buoyant microplastic particles at great ocean depths, several vertical transport mechanisms are under discussion with biofouling as one of the most referred. Biofouling is thought to increase the density of microplastic particles to the point that they sink to the deep sea, but this has mostly been shown on large microplastic particles ≥ 1 mm. However, although microplastics are defined as particles between 1 and 5000 μm, most microplastics are < 100 μm. In the ocean plastic particles continuously fragment, converting each "large" particle into several "small" particles, and particle abundance increases drastically with decreasing size. We argue that biofouling is not a reasonable transport mechanism for small microplastic particles ≤ 100 μm, which form the majority of microplastics. Biofilm density depends on its community and composition. A biofilm matrix of extracellular polymeric substances and bacteria has a lower density than seawater, in contrast to diatoms or large organisms like mussels or barnacles. We suggest that a small microplastic particle cannot host a biofilm community consisting of the heavy organisms required to induce sinking. Furthermore, to reach the deep sea within a reasonable timespan, a microplastic particle needs to sink several meters per day. Therefore, the excess density has to not only exceed that of seawater, but also be large enough to enable rapid sinking. We thus argue that biofouling cannot be an efficient vertical transport mechanism for small microplastic. However, biofouling of small microplastic may promote the likelihood of its incorporation into sinking marine snow and increase the probability of its ingestion, allowing its transport to depth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".