16s rRNA gene sequence analysis of the microbial community on microplastic samples from the North Atlantic and Great Pacific Garbage Patches
Bibliographic record
Abstract
ABSTRACT The exponential increase in plastic production has led to their accumulation in the environment, particularly in oceans, polluting these environments from the shore to the open ocean and even sea ice in the pole regions. We compared microbial communities on plastic particles, known as “Plastisphere”, collected from the Atlantic and Pacific oceans gyres in the Summer of 2019 and subsequently looked for potential plastic degraders. We applied a 16S rRNA amplicon sequencing approach to decipher differences and similarities in colonization behaviour between these two gyres. Two polymer types include plastics: polyethylene (PE) and polypropylene (PP). We found that microbes differed significantly between the two oceans and identified thirty-two differentially abundant taxa at the class level. Proteobacteria, Cyanobacteria and Bacteroidota were the most prominent relative abundant phyla in the two oceans. Finally, according to the current literature, we found 40 genera documented as potential plastic degraders. This study highlights the importance of the biogeographical location with respect to microbial colonization patterns of marine plastic debris, differing even in the open oceans. Furthermore, the wide distribution of potential plastic-degrading bacteria was shown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".