A seascape-scale habitat model to support management of fishing impacts on benthic ecosystems
Bibliographic record
Abstract
Minimizing fishing impacts on seafloor ecosystems is a growing focus of ocean management; however, few quantitative tools exist to guide seascape-scale habitat management. To meet these needs, we developed a model to assess benthic ecosystem impacts from fishing gear contact. The habitat impacts model is cast in discrete time and can accommodate overlapping fisheries as well as incorporate gear-specific contact dynamics. We implemented the model in the North Pacific using fishing data from 2003 to 2017, estimating that habitat in 3.1% of the 1.2 million km2 study area was disturbed at the end of the simulation period. A marked decline in habitat disturbance was evident since 2010, attributable to a single regulatory gear change that lifted trawl gear components off the seafloor. Running scenarios without these gear modifications showed these policies might have contributed to a 24% reduction in habitat disturbance since their implementation. Ultimately, model outputs provide direct estimates of the spatial and temporal trends of habitat effects from fishing — a key component of regulatory policies for many of the world’s fisheries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".