Lagrangian tracking of long-lasting plastic tags: From lobster fisheries in the USA and Canada to Macaronesia
Bibliographic record
Abstract
Plastic waste from the fishing industry, particularly lobster trap identification tags from the USA and Canada, poses a significant threat to marine ecosystems due to its resilience. This study unveils a novel link between North American fisheries and the appearance of these plastic tags in Macaronesia. Collected in the Azores and Canary Islands, these tags offer a unique insight into the sources and spatio-temporal scales of marine plastic pollution. Ocean model data indicates the Labrador Current and Gulf Stream as key forces transporting these tags. Virtual particle simulations show a small fraction reaching Macaronesia (4.12 % in the Azores, 0.76 % in the Canary Islands), suggesting real ocean drift. The Azores, with more collected tags, are more susceptible, and tags can reach Macaronesia in under a year. These findings underscore the urgency of better waste management and emphasize the role of citizen science in monitoring and combating marine pollution.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".