Architecture of a Community Infrastructure for Predicting and Analyzing Coastal Inundation
Bibliographic record
Abstract
The Southeastern Universities Research Association (SURA) has advanced the SURA Coastal Ocean Observing and Prediction (SCOOP) program as a multi-institution collaboration to design and prototype a modular, distributed system for real-time prediction and visualization of the coastal impacts from extreme atmospheric events, including hurricane inundation and waves. The SCOOP program vision is a community “cyberinfrastructure” that enables advances in the science of environmental prediction and coastal hazard planning. The system architecture is a coordinated and distributed network of interoperable, modularized components that include numerical models, information catalogs, distributed archives, computing resources, and network infrastructure. The components are linked over the Internet by standardized web-service interfaces in a service-oriented architecture (SOA). The design philosophy allows geographically disparate partnering institutions to provide complementary data-provider and integration services. The overall system enables coordinated sharing of resources, tools, and ideas among a virtual community of coastal and computer scientists. The distributed design builds on the notion that standards enable innovation, and seeks to leverage successes of the World Wide Web by creating an environment that nurtures interaction between the research community, the private sector, and government agencies working together on behalf of the nation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".