Data Integration With OGSA-DAI
Bibliographic record
Abstract
Researchers in the physical sciences continue to generate increasing amounts of data from simulations, sensors, and cataloging efforts such as DNA mappings and climate records. This increase in data has led to the need for need for new data management tools that assist researchers in managing the volume of data entailed as well as distributing the data across disk volumes and administrative domains. Distributing the data facilitates greater collab- oration between scientists and maximizes the data s value. These new data management requirements have led to the development of numerous data management systems. Two such systems are the Proactive Data Management System (PDMS) [4] and BioSimGrid [3]. BioSimGrid is a Data Grid project designed to distribute bio-molecular simulation results. PDMS is a data management tool developed by the University of Calgary Grid Research Centre (GRC) that facilitates management and movement of data using metadata rather than physical file locations. The proliferation of different data management systems leads to the need for an extensible framework that facilitates the integration of multiple data sources. OGSA-DAI [2] was designed to meet this goal. This document discusses the integration of services provided by PDMS or BioSimGrid with other data facilities such as databases using OGSA-DAI. The rest of this document is structured as follows. Sections 2 and 3 provide an overview of BioSimGrid and PDMS respectively. Section 4 discusses the architecture and limitations of the OGSA-DAI framework. The integration of BioSimGrid and PDMS are discussed in sections 5 and 6. Section 8 summarizes the document.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".