Simulation of Service Queue for Dental Doctor Patients at Kindergarten Hospital. IV 01.07.02 Binjai Using the Exponential Method
Bibliographic record
Abstract
One of the systems that can be simulated is a queue. The queuing system consists of a collection of customer servers, a queue that regulates service to customers. The utilization of queuing simulations is to optimize services by designing service resources so that services can be effective and efficient. In the queuing system there are several problems that often occur, for example queuing queues, the number of queues will cause problems such as bored customers, and discomfort will make customers dissatisfied with the service. The Application of the Exponensial Method in the Dentist Patient Service Queuing System There are 28 general populations in this study, namely all consumers at the Kesrem Binjai Hospital, while the target population is the population that is actually used as a data source for patients who queue at the Kesrem Binjai Hospital to get the best service. is the target population in this study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".