The submarine Congo Canyon as a conduit for microplastics to the deep sea
Bibliographic record
Abstract
The increasing plastic pollution of the world’s oceans represents a serious threat to marine ecosystems and has become a well-known topic garnering growing public attention. The global input of plastic waste into the oceans is estimated to be approximately 10 million tons per year and predicted to rise by one order of magnitude by 2025. More than 90% of the plastic that enters the oceans is thought to end up on the seafloor, and seafloor sediment samples show that plastics are concentrated in confined morphologies and sedimentary environments such as submarine canyons. These canyons are occasionally flushed by powerful gravity-driven sediment flows called turbidity currents, which transport vast volumes of sediment to the deep sea and deposit sediment in deep-sea fans. As such, turbidity currents may also transport plastics present in the canyon and bury plastics in deep-sea fans. These fans may therefore act as sinks for seafloor plastics. Here we present a comprehensive dataset showing the spatial distribution of microplastics in seafloor sediments from the Congo Canyon, offshore West Africa. Multicores taken from 16 locations along the canyon, sampled different sedimentary sub-environments including the canyon thalweg, canyon terraces, and distal lobe. Microplastics were extracted from the sediments by density separation and the polymer type, size, and shape of all individual microplastic particles were analysed using laser-direct infrared-spectroscopy (LDIR). Microplastic number concentrations in the sediments of the distal lobe are significantly higher than in the canyon, indicating that the Congo Canyon system is a highly efficient conduit for microplastic transport to the deep sea. Moreover, microplastic concentrations of >20,000 particles per kg of dry sediment were recorded in the lobe, which represent some of the highest ever recorded microplastic number concentrations in seafloor sediments. This shows that deep-sea fans can serve as hotspots and potential terminal sinks for seafloor microplastics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".