<em> </em>Current Research on Microbe-Plastic Interactions in the Marine Environment
Bibliographic record
Abstract
The microbial colonisers of plastics – the ‘plastisphere’ – can affect all interactions that plastics have with their surrounding environments. While only specifically characterised within the last 10 years, at the beginning of 2021 there were 140 primary research and 65 review articles that investigate at least one aspect of the plastisphere. We gathered information on the locations and methodologies used by each of the primary research articles, highlighting several aspects of plastisphere research that remain understudied: (i) the non-bacterial plastisphere constituents; (ii) the mechanisms used to degrade plastics by marine isolates or communities; (iii) the capacity for plastisphere members to be pathogenic or carry antimicrobial resistance genes; and (iv) meta-OMIC characterisations of the plastisphere. We have also summarised the topics covered by the existing plastisphere review articles, identifying areas that have received less attention to date – most of which are in line with the areas that have fewer primary research articles. Therefore, in addition to providing an overview of some fundamental topics such as biodegradation and community assembly, we discuss the importance of eukaryotes in shaping the plastisphere, potential pathogens carried by plastics and the impact of the plastisphere on plastic transport and biogeochemical cycling. Finally, we summarise the future directions suggested by the reviews that we have evaluated and suggest other key research questions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.027 |
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".