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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".