Simulation Of The Queue For Collecting Village Social Assistance Funds Using The Exponential Method
Bibliographic record
Abstract
Abstract
 This research aims to model and analyze the queuing process for collecting Village Bansus funds using an exponential distribution-based simulation method. The exponential method is used to describe the time variability between the arrival of the applicant and the service time in the queue. By utilizing simulation software, this research will examine various fund collection scenarios, including the effect of the number of service personnel on applicant waiting times and the efficiency of the fund collection process.
 It is hoped that the results of this simulation can provide recommendations for increasing efficiency in the process of collecting Village Bansus funds, including determining the optimal number of service officers. Apart from that, this research can also help village governments in planning and managing the Village Assistance program so that it can be more effective and responsive to community needs. This simulation provides important insight into how the queuing process can be optimized to improve the quality of service to applicants for Village Bansus funds.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".