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Record W4376102773 · doi:10.5281/zenodo.7920884

Overtime Management System for UNIX Manila Team Telus International Digital Solutions (TIDS)

2023· dissertation· en· W4376102773 on OpenAlexaboutno aff
Ma. Rossiya Anne L. Asinas

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedissertation
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOvertimeUnixComputer scienceOperations managementOperating systemEngineeringPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0040.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.008

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.

Opus teacher head0.029
GPT teacher head0.246
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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