A methodology for quantifying and characterizing litter from trash capture devices (TCDs) to measure impact and inform upstream solutions
Bibliographic record
Abstract
Trash capture devices (TCDs) are a rapidly evolving tool for municipal governments, non-governmental organizations, and industries to divert litter from our waterways. Here, we introduce protocols to initiate trash trapping projects to quantify and characterize captured anthropogenic litter based on a case study using Seabins. In addition, we have introduced a network for global data collection via TCDs. Our first protocol is a visual audit of the potential site to inform the type and location for TCD deployment. Our next two protocols quantify and characterize the litter captured by TCDs: (1) a simple protocol intended for daily monitoring and (2) a detailed protocol to characterize and quantify all large debris (>3 cm) and a subset of the small debris (2 mm–3 cm) caught in the devices. Using Seabins in the Toronto Harbour to test our methodology, we found that our subsampling methodology has a 6.9% error rate. Over a 19-week period, the Seabins captured ∼85 000 pieces of small debris. Our study highlights the utility of TCDs and proposes methods to realize this utility globally. TCDs should become more widespread and utilized as a triple threat: a cleanup tool, a data collection tool, and a platform for outreach and education.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".