Patterns and Emerging Trends in Global Ocean Health
Bibliographic record
Abstract
International and regional policies aimed at managing ocean ecosystem health need quantitative and comprehensive indices to synthesize information from a variety of sources, consistently measure progress, and communicate with key constituencies and the public. Here we present the second annual global assessment of the Ocean Health Index, reporting current scores and annual changes since 2012, recalculated using updated methods and data based on the best available science, for 221 coastal countries and territories. The Index measures performance of ten societal goals for healthy oceans on a quantitative scale of increasing health from 0 to 100, and combines these scores into a single Index score, for each country and globally. The global Index score improved one point (from 67 to 68), while many country-level Index and goal scores had larger changes. Per-country Index scores ranged from 41-95 and, on average, improved by 0.06 points (range -8 to +12). Globally, average scores increased for individual goals by as much as 6.5 points (coastal economies) and decreased by as much as 1.2 points (natural products). Annual updates of the Index, even when not all input data have been updated, provide valuable information to scientists, policy makers, and resource managers because patterns and trends can emerge from the data that have been updated. Changes of even a few points indicate potential successes (when scores increase) that merit recognition, or concerns (when scores decrease) that may require mitigative action, with changes of more than 10-20 points representing large shifts that deserve greater attention. Goal scores showed remarkably little covariance across regions, indicating low redundancy in the Index, such that each goal delivers information about a different facet of ocean health. Together these scores provide a snapshot of global ocean health and suggest where countries have made progress and where a need for further improvement exists.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".