RESTful web services for bridging presence service across technologies and domains: an early feasibility prototype
Bibliographic record
Abstract
A presence service enables the discovery and retrieval of, and subscription to changes in, an end user's context information. RESTful web services are now emerging as a lighter alternative to the so-called Big Web services. This article presents an early feasibility prototype of a RESTful web-services-based architecture. The architecture enables the discovery and retrieval of and subscription to changes in context information, independent of the technologies used in the end users' domains. Concretely, it enables end users with multiple presence accounts (e.g., MSN, Yahoo, Gmail) to publish context information related to the account(s) they are using at any given time. It also enables other end users or applications to retrieve this information by subscribing to any of the multiple accounts of the publisher. The project has demonstrated that RESTful web services are quite suitable for bridging services across technologies and domains. It has also demonstrated that a RESTful web services approach has several advantages over a traditional web services (also known as Big Web services) approach. However, more functionality needs to be added to the prototype before market introduction is contemplated. The lessons learned are discussed and the missing functionalities are identified.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".