The “Microplastome” – A Holistic Perspective to Capture the Real-World Ecology of Microplastics
Bibliographic record
Abstract
Microplastic pollution, an emerging pollution issue, has become a significant environmental concern globally due to its ubiquitous, persistent, complex, toxic, and ever-increasing nature. As a multifaceted and diverse suite of small plastic particles with different physicochemical properties and associated matters such as absorbed chemicals and microbes, future research on microplastics will need to comprehensively consider their multidimensional attributes. Here, we introduce a novel, conceptual framework of the "microplastome", defined as the entirety of various plastic particles (<5 mm), and their associated matters such as chemicals and microbes, found within a sample and its overall environmental and toxicological impacts. As a novel concept, this paper aims to emphasize and call for a collective quantification and characterization of microplastics and for a more holistic understanding regarding the differences, connections, and effects of microplastics in different biotic and abiotic ecosystem compartments. Deriving from this lens, we present our insights and prospective trajectories for characterization, risk assessment, and source apportionment of microplastics. We hope this new paradigm can guide and propel microplastic research toward a more holistic era and contribute to an informed strategy for combating this globally important environmental pollution issue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".