Chasing plastic storms: Assessing atmospheric microplastic deposition by a ‘pulse event’ of tropical storm Fiona in Eastern Canada
Bibliographic record
Abstract
Atmospheric processes and extreme weather events are pathways for the global distribution and deposition of microplastics. Despite the global prevalence of meteorological events, our understanding of atmospheric microplastic pathways and fall-out to the terrestrial, aquatic and marine environment resulting from storms and severe events is limited. In this study, we geospatially consider a unique time series of the movement of atmospheric microplastics and anthropogenic microdebris during an extreme tropical storm in Atlantic Canada. The large tropical storm Fiona was recorded as the deepest cyclone that caused the worst financial damage on record for Eastern Canada during its’ landfall in Nova Scotia (September 22nd to 24th 2022). We collected a unique timeseries of passive deposition samples of atmospheric fall-out before, during, and after storm Fiona. Through micro-Raman spectroscopy and Nile Red fluorescence techniques, we chemically and morphologically characterized particles and quantifies the microdebris and microplastic fallout resulting from the storm. Using back trajectory modelling we aim to identify storm related sources and movement of these particles prior to deposition. As climate change drives increased storm frequency and intensity, it becomes more critical than ever to obtain meteorological baseline data of these pathways.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".