Bibliographic record
Abstract
The Infrastructure Interdependencies Simulation (I2Sim) team led by Dr. José R. Martí at the University of British Columbia has been researching the hidden interdependencies between complex infrastructures for several years [1]. The I2Sim platform was developed on the foundation of Matlab Simulink and has been significantly improved by researchers and engineers since the first version of the toolbox created in 2007 [2]. The current version of the I2Sim toolbox has versatile capabilities on many applications such as disaster response, resource optimization, financial management, etc. For disaster response application, in particular, the I2Sim team has formed a group of engineers in cooperation with the University of Western Ontario and the University of New Brunswick to develop the Disaster Response Network Enabled Platform (DR NEP). DR NEP is a distributed platform that communicates through an Enterprise Service Bus (ESB)utilizing the state-of-the-art Lightpath services provided by CANARIE. With advanced computing power and high speed network connections, DR NEP is able to integrate I2Sim with other simulators and services, which are physically located all over Canada, to perform real time simulations and provide decision support for emergency responders. To further enhance user experience and improve the user interface for emergency responders, Web services were used in the project to create a web-based platform to display the simulation results on web pages and GIS systems, such as Google Earth. This platform enables responders to update and exchange information from standard web browsers and Google Earth. Simulation experts can use the website to control simulation and view simulation results and feedback from the website. A test case which involves the 2011 Tohoku earthquake incident in Japan is included in this report to demonstrate the simplicity of the user interface and the contribution of the web service to DR NEP. In addition, "what-if" scenarios were conducted on the model to explore better emergency responding strategies. The results from the simulation were studied and analyzed in detail. DR NEP is a fully functioning platform with complete components. With sufficient information provided by emergency responders or local resource management facilities, a complete model can be constructed for simulation and study. The next phase of the development would be model automatic creation, ergonomic user interface design, improvement on role-based access and model validation methodology development. Recommendations to those problems are included accordingly. As a member of the DR NEP team, I have been involved in most of the phases of the platform development. My main contribution to the team includes designing part of the table structures in database schema, implementing the web services for data visualization (Google Earth and the associated web services) and constructing the Japan Sendai City model.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.071 |
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".