The value of bottom trawling in Europe
Bibliographic record
Abstract
<title>Abstract</title> Commercial bottom trawl and dredge fisheries are active across much of Europe, and their geographic footprint is extensive. More than half of seabed area is trawled every year in some parts of Europe. But these fisheries remain contentious; significant ecological and economic damages have been well documented. Yet, they remain a source of food and provide jobs and economic revenue. Considering recent pushes to ban or limit bottom trawling in European countries, we explore how the costs associated with this practice compare to the benefits it provides. We find that society is losing out to the private sector, largely because of the significant climate impacts associated with the churning of the seafloor sediment by bottom trawling. Further, we show that bottom trawling occurs in a significant portion of Marine Protected Areas (MPAs) across Europe. We argue that phasing out bottom trawling in MPAs could yield meaningful net benefits.
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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 | 0.001 | 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 | 0.001 | 0.009 |
| 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".