Where Have You Been? Backtracking Microplastic to Its Source Using the Biomolecular Composition of the Ecocorona
Bibliographic record
Abstract
Abstract Microplastics are a diffuse contaminant with various global sources, pathways, and sinks. This study aimed to backtrack microplastics across environments using metaproteomic and eDNA metabarcoding information stored within the ecocorona. Pristine polyamide (PA) fibers, polyethylene terephthalate (PET) fibers and fragments, and PET and PA preincubated in bovine serum albumin (BSA) were deployed into a tank housing Penaeus monodon to develop an ecocorona. Upon collection, BSA was detected within the ecocorona, along with P. monodon proteins, using mass spectrometry. BSA preincubation influenced the diversity and abundance of ecocorona proteins with pristine microplastics having more significantly enriched proteins. Most ecocorona proteins reflected the marine environment, confirming that the protein assemblage on microplastics records environmental signatures. Microplastic tracking was validated using polyethylene plastics unintentionally discharged from an aquaculture facility into Moreton Bay and collected after 7 days. Orthogonal Partial Least Square models predicted the source with 69–92% accuracy based on 16S eDNA taxa and 69–123% accuracy based on untargeted metaproteomics. Several identified taxa from both analyses were specific to the aquaculture source, including genera Leucothrix and Rugeria and species Salmo salar and P. monodon. Overall tracking of microplastics using the ecocorona proved effective over short time scales and reliably reflected the surrounding biological milieu.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".