DILIGENT - D4.1.3 - Annex 1 - Building Digital Libraries on Service Oriented Architectures: Challenges, Experiences, and Results. Tutorial at 2007 JCDL Conference, Vancouver, 19 June 2007
Bibliographic record
Abstract
The tutorial discusses the core ideas of building digital libraries on distributed infrastructures and the related architectural options. The main part of the tutorial revolves around how core DL functionality like Digital Library Management, Content and Collection Management, Metadata Management and Brokering, Search and Retrieval and Advanced Services (Personalization, Annotation, etc.) can be built by exploiting P2P and Grid technological frameworks. For each of the topics the key challenges are discussed together with possible solutions for the challenges and the lessons learned in implementing these solutions in concrete projects. The solutions are illustrated with concrete examples and demos from the projects DILIGENT, BRICKS, and DELOS as well as from other DL-related projects. Target Audience: The tutorial is mainly targeted towards researchers and practitioners that are working in the area of digital libraries, digital library architectures, and innovative digital library services. Furthermore, the tutorial will also be an opportunity for other players to gain insights in the area of current developments in the area of next generation digital library architectures and new applications of Grid technology and the Peer-to-peer paradigm. Level of experience required: Introductory to intermediate.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.113 | 0.074 |
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".