Bibliographic record
Abstract
The GridBASE framework for database-driven grid computing is presented. The design and a prototype implementation of the framework is discussed. Industry-strength database technology plays a key role in the design of the framework. The database is used as a scalable, reliable and remotely accessible component both for storing and organizing the configuration information of the grid, and for managing information related to the grid users and the jobs and tasks they submit for execution. Other system components are worker nodes, a simple resource broker, a grid operator console, and application clients. In analogy with electrical power grids, a clear distinction is made in our design between the role played by grid users on the one hand, who develop and submit application code but are otherwise mostly isolated from resource deployment and selection, and the role played by the grid operator on the other hand, who is responsible for providing computing resources and assuring system availability and maintenance. Application code can be written in any language, and simple workflow support is provided. In our prototype implementation we experiment with code delivery and input and output file delivery via the database component. Our approach is based on decentralization and implemented in Java, leading to a lightweight, portable and scalable grid computing solution that is especially suited for parallel bioinformatics. Deployment of GridBASE on Ontario's SHARCNET and application to virtual experiments in RNA folding statistics are described.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".