New framework reveals gaps in US ocean biodiversity protection
Bibliographic record
Abstract
Human activities threaten Earth's biodiversity and its contributions to human well-being. In the ocean, our poor understanding of how biodiversity is distributed limits its management and protection, necessitating reliance on weak abiotic proxies. Here, we propose a scientific framework for assessing marine biodiversity at multiple spatial scales, which exposes gaps in biodiversity knowledge and protection. The framework prioritizes ecologically and societally important taxa, characteristics of effective networks, and existing data. Applying the framework to assess biodiversity inside and outside US marine protected areas, we reveal that these areas contain a fraction of the biodiversity found in US waters. We show that none of the nation's 24 marine ecoregions meet all criteria for an effective protection network and that biodiversity coverage in protected areas varies among regions and taxa. This marine biodiversity assessment highlights concrete recommendations for more strategic protection and validates a scientific framework generalizable to other spatial management uses.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".