Graphical Spreadsheet Simulation of Queues
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
Graphical representations of spreadsheet queueing simulations can be used to teach students about queues and queueing processes. A customer graph shows the experience of every individual customer in a queue, based on arrival time, start of service, end of service, and showing clearly the length of time in queue and service time for each individual customer. The cumulative effect is powerful, illustrating how one long service time (or short interarrival time) can cause delays for many succeeding customers. The server graph (a Gantt chart) shows the experience of each server, illustrating how customers stack up, and the nature of periods of idle time. The graphs are linked to a spreadsheet simulation and update instantly when the simulation is replicated. The graphs illustrate the complete evolution of a queue (which simulation animations cannot do) and help provide a holistic view of queues. They can be used to teach students about the nature of queues and support active learning where the students articulate for themselves the cause of queue behaviors.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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