Improving Ocean Management through the Use of Ecological Principles and Integrated Ecosystem Assessments
Bibliographic record
Abstract
The US National Ocean Policy calls for ecosystem-based management (EBM) of the ocean to help realize the vision advanced in the 2010 Executive Order on the Stewardship of the Ocean, Our Coasts, and the Great Lakes. However, no specific approach for incorporating EBM into planning was provided. We explore how a set of ecological principles and ecosystem vulnerability concepts can be integrated into emerging comprehensive assessment frameworks, including Australia's National Marine Bioregional Assessments, California's Marine Life Protection Act Initiative's regional profiles, Canada's Eastern Scotian Shelf Integrated Management Initiative, and the US National Oceanic and Atmospheric Administration's (NOAA) Integrated Ecosystem Assessment (IEA) program, to transition to ecosystem-based ocean planning. We examine NOAA's IEA framework to demonstrate how these concepts could be incorporated into existing frameworks. Although our discussion is focused on US ocean policy, comprehensive ecological assessments are applicable to a wide array of management strategies and planning processes.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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 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".