RSS based CERN Alerter. Information broadcast to all CERN offices.
Bibliographic record
Abstract
Nearly every large organization uses a tool to broadcast messages and information across the internal campus (messages like alerts announcing interruption in services or just information about upcoming events). These tools typically allow administrators (operators) to send "targeted" messages which are sent only to specific groups of users or computers, e/g only those located in a specified building or connected to a particular computing service. CERN has a long history of such tools: CERNVMS€™s SPM_quotMESSAGE command, Zephyr and the most recent the NICE Alerter based on the NNTP protocol. The NICE Alerter used on all Windows-based computers had to be phased out as a consequence of phasing out NNTP at CERN. The new solution to broadcast information messages on the CERN campus continues to provide the service based on cross-platform technologies, hence minimizing custom developments and relying on commercial software as much as possible. The new system, called CERN Alerter, is based on RSS (Really Simple Syndication) for the transport protocol and uses Microsoft SharePoint as the backend for database and posting interface. The windows-based client relies on Internet Explorer 7.0 with custom code to trigger the window pop-ups and the notifications for new events. Linux and Mac OS X clients could also rely on any RSS readers to subscribe to targeted notifications. The paper covers the architecture and implementation aspe cts of the new system.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.200 | 0.271 |
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".