Creating and Using Data Portals to Support Ocean Planning: Challenges and Best Practices from the Northeast United States and Elsewhere
Bibliographic record
Abstract
As more ocean plans are developed and adopted around the world, the importance of accessible, up-to-date spatial data in the planning process has become increasingly apparent. Many ocean planning efforts in the United States and Canada rely on a companion data portal–a curated catalog of spatial datasets characterizing the ocean uses and natural resources considered as part of ocean planning and management decision-making.Data portals designed to meet ocean planning needs tend to share three basic characteris- tics. They are: ocean-focused, map-based, and publicly-accessible. This enables planners, managers, and stakeholders to access common sets of sector-speci c, place-based information that help to visualize spatial relationships (e.g., overlap) among various uses and the marine environment and analyze potential interactions (e.g., synergies or con icts) among those uses and natural resources. This data accessibility also enhances the transparency of the planning process, arguably an essential factor for its overall success.This paper explores key challenges, considerations, and best practices for developing and maintaining a data portal. By observing the relationship between data portals and key principles of ocean planning, we posit three overarching themes for data portal best practices: accommodation of diverse users, data vetting and review by stakeholders, and integration with the planning process. The discussion draws primarily from the use of the Northeast Ocean Data Portal to support development of the Northeast Ocean Management Plan, with additional examples from other portals in the U.S. and Canada.
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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 | 0.062 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".