A Cross Disciplinary Framework for Cost-Benefit Optimization of Marine Litter Cleanup at Regional Scale
Bibliographic record
Abstract
Litter cleanup and disposal management in the marine environment are increasingly subject to public scrutiny, government regulation and stakeholder initiatives. In practice, ongoing efforts and new investment decisions, for example in new cleanup technologies, are constrained by financial and economic resources. Given budgetary restrictions, it is important to optimize decision-making using a scientific framework that takes into account the various effects of investments by combining multiple scientific perspectives and integrating these in a consistent and coherent way. Identifying optimal levels of marine litter cleanup is a challenge, because of its cross-disciplinary nature, involving physics, environmental engineering, science, and economics. In this paper, we propose a bridge-building, spatial cost-benefit optimization framework that allows prioritizing where to apply limited cleanup efforts within a regional spatial network of marine litter sources, using input from the maturing field of marine litter transport modeling. The framework also includes ecosystem functioning in relation to variable litter concentrations, as well as the potentially non-linear cost-efficiency of cleanup technologies. From these three components (transport modeling, ecosystem functioning, cleanup-effectiveness), along with litter source mapping, we outline the optimal cleanup solution at any given ecological target or economic constraint, as well as determine the cleanup feasibility. We illustrate our framework in a Baltic and Mediterranean Sea case study, using real data for litter transport and cleanup technology. Our study shows that including pollution Green's functions is essential to assess the feasibility of cleanup and determine optimal deployment of cleanup investments, where the presented framework combines physical, economical, technological and biological data consistently to compare and rank alternatives.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 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".