Context-aware service selection based on dynamic and static service attributes
Bibliographic record
Abstract
Context-aware applications are able to use context, which refers to information about the surrounding environment, to provide relevant information and/or services to the user. A context-aware application may need to make use of existing services (e.g., a print service). There may be several possible choices of services. The context-aware application should be able to discover and select a service that considers context (e.g., current user location). Existing architectures and protocols for service discovery, however, are not suitable for doing so. Contextual information, by its very nature, is dynamic, reflecting the current state and conditions of the application, its user, or its operating environment. Existing architectures and protocols for service discovery, however, tend to assume the world is static, with attributes describing services offered never changing. If attributes are allowed to change, the approaches do not provide the architectural mechanisms required to update them; dynamic attributes with no means of updating are static for all intents and purposes. To support context-aware service discovery and selection, a better approach is required. This paper discusses one possible approach that is based on existing techniques.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".