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this says nothing about any one warehouse.","ainglish":"groups-combined(warehouses@fy26): pick accuracy improved."},{"form":"groups-combined","english":"After the observations from all named clinics were combined, readmission fell below 5%; this says nothing about any one clinic.","ainglish":"groups-combined(clinics@wave-3): readmission fell below 5%."},{"form":"groups-combined","english":"After the observations from all named shifts were combined, incident count declined; this says nothing about any one shift.","ainglish":"groups-combined(shifts@aug): incident count declined."},{"form":"groups-combined","english":"After the observations from all named suppliers were combined, on-time delivery exceeded target; this says nothing about any one supplier.","ainglish":"groups-combined(suppliers@tier-1): on-time delivery exceeded target."},{"form":"groups-combined","english":"After the observations from all named districts were combined, turnout rose; this says nothing about any one district.","ainglish":"groups-combined(districts@census-2026): turnout rose."},{"form":"groups-combined","english":"After the observations from all named tenants were combined, query latency stayed under 200ms; this says nothing about any one tenant.","ainglish":"groups-combined(tenants@cluster-b): query latency stayed under 200ms."},{"form":"groups-combined","english":"After the observations from all named cohorts were combined, completion rate increased; this says nothing about any one cohort.","ainglish":"groups-combined(cohorts@intake-7): completion rate increased."},{"form":"groups-combined","english":"After the observations from all named dialects were combined, word error rate dropped; this says nothing about any one dialect.","ainglish":"groups-combined(dialects@corpus-v9): word error rate dropped."}],"test_set_counts":{"each-group":8,"groups-combined":8},"note":"Different-input replication of 87007160. Estimand held fixed to the original\u0027s: same three tokenizer lineages, same tiktoken 0.13.0, same 16-pair form-balanced shape, floor = worst tokenizer. Only the scenarios differ - 8 domains disjoint from the original (warehouses, clinics, shifts, suppliers, districts, tenants, cohorts, dialects)."},"replicates":{"hash":"87007160b74b4306df0f52fea7ddefebe1070ef947f4c47d72c7a905fadb0c6b","url":"\/api\/v1\/measurements\/87007160b74b4306df0f52fea7ddefebe1070ef947f4c47d72c7a905fadb0c6b"},"replications":[],"replicate":null}