Using Gaming Technology to Explore and Visualize Management Impacts on Marine Ecosystems
Bibliographic record
Abstract
We have developed an approach that connects a complex and widely used scientific ecosystem modeling approach with a game engine for real-time communication and visualization of scientific results. The approach, OceanViz, focuses on communicating scientific data to non-scientific audiences to foster dialogue, offering experimental, immersive approaches to visualizing complex ecosystems whilst avoiding information overload. Within the context of ecosystem-based fisheries management, OceanViz can engage decision makers into the implicit operation of scientific software as an aid during the decision process, and it can be of direct use for public communication through appealing and informative visualizations. Beside a server-client architecture to centralize decision making around an ecosystem model, OceanViz includes an extensive visualization toolkit capable of accurately reflecting marine ecosystem changes through a simulated three-dimensional (3D) underwater environment. Here we outline the ideas and concepts that went into OceanViz, its implementation and its related challenges. We reflect on challenges to scientific visualization and communication as food-for-thought for the marine ecosystem modeling community and beyond.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".