Time required to move cash through slot machines
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to present an empirical study on the time needed to load and disburse cash using bill validators on slot machines and stand-alone cash dispensers in casinos in British Columbia under a Ticket In Ticket Out (TITO) system. Design/methodology/approach Testing took place over two days, using 18 machines. The results were extrapolated to estimate the approximate time required to process $1,000,000 with different average bill amounts in the cash mix and three different bill validator machines in common use. The average value per bill using the cash mix used by the public in the casino was $33.11 [standard error (SE) $2.11]. Findings The mean time/accepted note ranged from 4.12 to 9.65 s, depending on bill validator type. This implies that the time needed to load $1,000,000 onto credit slips using bill validators on slot machines ranges from 35 to 81 h, excluding rest breaks and other breaks. The time needed to redeem $1,000,000 is estimated to be 3 h. Practical/implications The implications of these finding for illicit actors to successfully launder large amounts of cash are discussed. Given the time needed to physically handle the cash, and other control systems currently in use in casinos in British Columbia, processing large amounts of cash using bill validators on slot machines would require a highly organized team that would find it difficult to elude detection. Originality/value The trial results provide a baseline estimate to be used going forward when investigating or proposing money laundering methodologies that include slot machines.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it