Adaptable Discovery and Ranking of Context-Dependent Services
Bibliographic record
Abstract
This paper emphasizes the role of contextual information and legal rules in publishing services, formulating contracts, discovering services, and their impact on ranking and adaptability. We use Configured Service concept, which is a package that bundles together service functionality, service contract, and service provision context. Service providers only publish Configured Services in a service registry. Service requesters query the registry to discover available services that can match their requirements. Often there is a semantic gap between the service query and the services in the registry. To deal with this, we discuss three query types. The discovery processes, employing different matching processes that are appropriate for the query types, will rank the services in order to enable the requester choose the most relevant service(s). Ranking is also essential when the number of matching's is large. We identify the different situations that call for rediscovery and re-ranking of service queries. We include a brief account of formalism, within which all these activities are precisely described.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".