Modelling the Sharing of Resources across Collaborative Sessions
Bibliographic record
Abstract
Service-oriented architectures can be used to provide multiple simultaneous sessions to users that wish to communicate over a variety of media. This gives rise to rich, highly effective communication sessions that can greatly enhance userspsila interaction. For example, a health services virtual organization seeks to use such tools for a variety of purposes: virtual patient simulation, anatomical visualization and virtually sharing cadaveric dissections. We propose SAVOIR, Service-oriented Architecture for Virtual Or- ganization Resource and Infrastructure for this task, where tools and applications are resources and they can be accessed and controlled via Web Services. The purpose of this paper is to present a method for modeling sessions in SAVOIR by using Web Ontology Language (OWL). We express in OWL 1.1 constraints on when sessions can and cannot be run or cannot be run concurrently with the ses- sions now running. There are several types of violations: aggregate bandwidth may exceed capacity, network infras- tructure may not be available, too many users may want to access a limited shared resource, etc. The session scheduler depends on the OWL 1.1 description logic reasoner to evaluate the session for violation of these constraints before the session is allowed to be scheduled.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".