Recent advances in understanding the environmental footprint of trawling on the seabed
Bibliographic record
Abstract
Bottom trawling accounts for nearly a quarter of wild-capture seafood production, but it is associated with physical disturbance of the seabed leading to changes in benthic abundance, habitat structure, and biogeochemical processes. Understanding the processes of benthic depletion and recovery in relation to different types of fishing gears, and in different seabed types, is an important pre-requisite to inform appropriate management measures to limit or reduce the effects of trawling on the seabed. The combined approaches of meta-analysis and modelling that link fishing-gear penetration of the seabed to benthic depletion, and recovery to taxon longevity, have enabled the development of a modelling framework to estimate relative benthic status in areas subject to trawling. Such estimations are highly sensitive to the spatial resolution at which fishing footprint (trawl track) data are aggregated, and this leads to overinflated estimates of fishing impacts on benthos when coarse-level aggregation is applied. These approaches present a framework into which other “sustainability” criteria can be added, e.g., the consideration of carbon footprints of fishing activities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".