The queue page assumes twelve staff process fifteen cases each per day, capacity one hundred eighty. Its formula amplifies twelve hundred incoming complaints by one plus persistence point eight five times point eight, giving two thousand sixteen demand and excess eighteen hundred thirty six. Bars use one tenth pixel per case per day. The initial stored excess ten twenty omits that retry multiplier. Offline p5 is unavailable, so startup throws before recomputation; native inline sliders still recompute and actual JSON still exports. Persistence eighty five percent is not the particle retry probability: that separate visual probability is point eight five times point six, fifty one percent. Neither coefficient is measured administrative behavior. No appeals, complaints or government requests are submitted. Backlog is incremented whenever telemetry calculates, using rounded daily excess times point zero five. Raising staff to fifty gives capacity seven hundred fifty, excess twelve hundred sixty six and increment sixty three. Starting from forty three twenty, one native change yields forty three eighty three tickets. Bars compare those totals at one tenth pixel per ticket. Repeating a control event can keep growing backlog even without simulated time passing. This is event-count mutation, not a continuous queue or a measured forecast. Resolution caption divides backlog by staff capacity, five point eight days after this edit, then uses an assumed citation factor to derive friction. Export captures that synthetic state. The rendering failure is preserved rather than replaced with a fabricated operational visualization. With original staffing, rate limiting reduces incoming volume by an assumed thirty five percent and triage raises capacity by an assumed thirty percent. Demand becomes thirteen ten point four and capacity two thirty four, still over a thousand excess cases per day. Bars encode both at one fifth pixel per case. These switches therefore do not stabilize that default queue, contrary to the earlier video claim. The escalation-cap flag appears in the policy label and particle behavior but is absent from demand and capacity arithmetic. Its display is not evidence of quantitative effectiveness. No current legal process, policy recommendation or empirical staffing guarantee is established. Native slider and JSON, four-width allocation and all-footer placement are only local subset checks.