Overtime Management System for UNIX Manila Team Telus International Digital Solutions (TIDS)
Bibliographic record
Abstract
The Unix Manila team is a part of Telus International Philippines, Inc.'s Digital Solutions group and consists of 33 systems administrators who provide 24/7 technical support for maintaining the UNIX servers of Telus Communications Canada. To ensure high-quality service to customers, the team occasionally works overtime, which includes covering for absent team members, a practice known as "fill-in." The team previously used a makeshift tool built in Microsoft Sharepoint to manage fill-in requests and track leave projections, however, due to Telus's recent partnership with Google, most applications were migrated to the Google platform and in September 2022, Microsoft Sharepoint was decommissioned. To replace this, the team developed the Overtime Management System (OMS), a web-based system built in PHP, CSS, HTML, JavaScript, Bootstrap, and MariaDB. In addition to the fill-in application and leave tracking, OMS also includes new features such as offset tracking, additional resource management, and report generation. The system is designed to be scalable, reusable, and flexible, allowing for easy migration in the event of future changes to the organization's infrastructure.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.035 |
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".