Bibliographic record
Abstract
The task of imaging lab computers can be a complicated and manual process. University of Calgary Information Technologies Lab Services (IT Lab Services) has endeavored to streamline this process by developing an in-house system. This system addresses the issues of collecting inventory information, loading an image on to a PC, and configuring the PC after imaging. A custom version of the Windows Preinstallation Environment by Bart Lagerweij (BartPE) is used to bootstrap the system. BartPE can be loaded from a CD or DVD on older systems and from select USB keys on newer generations. Within the bootstrap, custom scripts are executed to record inventory information, network speed, as well as starting the Ghost process. Upon image deployment completion, the automated configuration process begins at the first reboot. A custom script which we term Declone names the PC from our inventory information and joins the domain. Other scripts configure the task scheduler and other post-imaging tasks. The post-imaging process is mostly standalone, only requiring network access to join the domain. It has also been modified for the use of pGina by XPA Systems, our LDAP based authentication system. Another set of scripts are executed to verify proper naming of the newly imaged PC. The naming component is extremely important as many services depend on having the correct computer name. This in-house imaging system has greatly improved the efficiency of our imaging process and has stood the test of time. We currently use it with Windows 2000 and XP Professional.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.819 | 0.710 |
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".