Bibliographic record
Abstract
ServiceBC is British Columbia’s central government organization for delivering government information and services to citizens. It plays an important role in providing front line support for all the programs and services to government health and human service agencies. It uses contact center services from various clients across Canada to serve its residents. MAXIMUS Canada is one such client which provides its omni-channel contact center agent’s support to ServiceBC for all healthcare needs. Currently, contact center at MAXIMUS Canada provides three modes of channels phone, text, email for its residents to communicate with agents. Using these channels residents still need to visit their nearby ServiceBC office for MSP validation, filling health related forms, submitting documents as part of verification, adding additional information on to their profile and so on. This process delays overall service time for end users especially residents living in remote locations. In this project, two new channels Video and Web chat along with Co-browse tool have been incorporated. With these, residents will be able to easily approach ServiceBC agents for their queries and quickly validate their health cards (MSP) without any delays virtually over a video chat, fill ServiceBC health related forms, submit documents as part of verification, add additional information on to their profile, share their screen to an agent and so on. As part on integration Cafe-X component, Fusion Web gateway helps in removing signaling complexity between client app and SIP endpoints and plays a major role in signalling conversions. Another important component, Fusion Media Broker helps to convert and adapt media between external clients and enterprise devices; for inbound traffic, simplifies & limits RTP for legacy devices; for outbound traffic adds additional features for browser & mobile clients. With the help of Fusion Web gateway, the incoming HTTP traffic is converted to the SIP signalling. Once a resident selects an option for communicating on ServiceBC website the request is directed through the Cafe-X solution. The request is sent to the MAXIMUS contact center via SIP trunks and is placed through respective queue for a ServiceBC agent. Once an available agent accepts the request there is a communication link is established over a secured network using reverse proxy server. In this project, the integration is done in few steps, requirements gathering, building UML diagrams, building use cases, product implementation. Once all the Cafe-X components are incorporated, all the enhanced features are tested through rigorous test phases exploratory testing, integration testing, functional testing followed by Business Acceptance testing. Defects for each phase are captured and tracked using defect tracking process in StarTeam based on priority to obtain a system with no major issues and least number of minor issues/warnings. The video chat feature is implemented in production after all the final test results are “pass” with no major defects. As a scope of this project, one ServiceBC agent can handle a maximum of one video chat/phone, three web chats, three texts, one co-browse session in parallel which can be extended in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.279 | 0.089 |
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".