Modeling Web maintenance centers through queue models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it