Wind driven transport of macroplastic debris in a large urban harbour measured by GPS-tracked drifters
Bibliographic record
Abstract
The transport pathways of floating plastic debris in Toronto Harbour, Ontario, Canada, were assessed using a series of GPS-tracked drifter bottles. The drifter trajectories were largely controlled by winds, and they could traverse the 2 km wide harbour within a day. The average ratio of drifter speed to wind speed (the wind factor) is consistent with values of 2-5 % used in modelling dispersion of marine debris. However, significant variability in wind factors meant some drifters travelled 2-5 times faster than expected in small waterbodies (Toronto Harbour), and as much as 7 times faster in large waterbodies (Lake Ontario). Importantly, based on our calculated wind factor equations and the coincident accumulation of our drifters with real plastic debris, we can justify the use of wind factors when studying plastic debris transport. Most (75 %) of the drifters that were released in the harbour, stayed within the harbour, accumulating downwind. However, 14 of all 66 drifters escaped Toronto Harbour, where ∼70 % escaped through the West Gap while ∼30 % escaped via the Outer Harbour. One drifter made a 290 km journey across Lake Ontario in a period of 14 days, demonstrating that Toronto is a potential source of plastic debris throughout Lake Ontario. • 66 GPS-drifter tracks quantified wind-driven transport of floating plastic debris. • Out of 66 drifters overall, 3 drifters travelled 100–290 km in Lake Ontario. • 25 % of the 63 drifters deployed within the harbour had escaped the harbour. • Our GPS-drifters emulate real debris and travel faster than modelled speeds.
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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 | 0.000 | 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".