Bibliographic record
Abstract
For many years CERN had a very sophisticated print server infrastructure which supported several different protocols (AppleTalk, IPX and TCP/IP) and many different printing standards. Today’s situation differs a lot: we have a much more homogenous network infrastructure, where TCP/IP is used everywhere and we have less printer models, which almost all work using current standards (i.e. they all provide PostScript drivers). This change gave us the possibility to review the printing architecture aiming at simplifying the infrastructure in order to achieve full automation of the service. The new infrastructure offers both: LPD service exposing print queues to Linux and Mac OS X computers and native printing for Windows based clients. The printer driver distribution is automatic and native on Windows and automated by custom mechanisms on Linux, where the appropriate Foomatic drivers are configured. Also the process of printer registration and queue creation is completely automated following the printer registration in the network database. At the end of 2006 we have moved all (~1200) CERN printers and all users' connections at CERN to the new service. This paper will describe the new architecture and summarize the process of migration.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.325 | 0.279 |
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".