Modeling Web maintenance centers through queue models
Bibliographic record
Abstract
The Internet and the World Wide Web's pervasiveness are changing the landscape of several different areas, ranging from information gathering/managing and commerce to software development, maintenance and evolution. Traditional telephone-centric services, such as ordering of goods, maintenance/repair intervention requests and bug/defect reporting, are moving towards Web-centric solutions. This paper proposes the adoption of queuing theory to support the design, staffing, management and assessment of Web-centric service centers. Data from a mailing list archiving a mixture of corrective maintenance and information requests were used to mimic a service center. Queuing theory was adopted to model the relation between the number of servers and the performance level. Empirical evidence revealed that, by adding an express lane and a dispatcher service time, the variability is greatly reduced and more complex business rules may be implemented. Moreover, express-lane customers experience a reduction of service time, even in the presence of a significant percentage of requests erroneously routed by the dispatcher.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".