VAQoS
Bibliographic record
Abstract
During the last few years, Value Added Web Services (VAWS) are increasingly becoming a hot issue in both research and industry. With the abundance of VAWS providers, Quality of Service (QoS) is a key factor to allow potential clients to differentiate between providers. In this paper, we propose a new architecture, called VAQoS, for managing and assuring QoS provision for VAWS. This architecture performs management of QoS by: (1) allowing providers to extend the service description with QoS-centered annotations, (2) including a validation process that enables providers to test their service interfaces as well as the level of QoS they can provide prior to publishing the service, (3) allowing clients to express their required functionalities with QoS requirements; (4) providing support for QoS negotiation between clients and providers, (5) allowing monitoring of the agreed QoS between clients and providers, and therefore, detecting any QoS violation; (6) providing an application programming interface that shields the application of the provider and client from the complexity of managing QoS specification, QoS publication, and QoS discovery. A first prototype of VAQoS is developed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.021 |
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".