<result> <column_name>Aces</column_name> <family>additive</family> <subtype>real_additive</subtype> <confidence>0.88</confidence> <requires_user_confirmation>false</requires_user_confirmation> <sum_meaningful>true</sum_meaningful> <signed>false</signed> <reducers> <primary_reducer>sum</primary_reducer> <valid_reducers>sum, min, max, mean, median, count, distribution</valid_reducers> <excluded_reducers>product (not a ratio), geomean (not a ratio)</excluded_reducers> <average_semantics>arithmetic_mean</average_semantics> </reducers> <inner_outer_measure> <inner_measure_reducer>sum</inner_measure_reducer> <outer_measure_reducer>sum</outer_measure_reducer> </inner_outer_measure> <multiplicative_partner_derivable>true</multiplicative_partner_derivable> <reasoning> The table is PATH with a Date sequence axis, so the full analysis applies. Aces is a non-negative integer-valued count of events per entity per date (mean about 4.9, median 4, max 67). It is centered far from 1.0, contains zeros, has a wide unbounded upper range, and has no ratio naming or unit, so it is not multiplicative. The positive log_symmetry_gain is expected for right-skewed count data and is overridden by the count semantics and center of mass. Counts accumulate when volumes merge, so sum is meaningful and the column is real_additive, not an intensive level. A per-match count could be argued as ordinal_additive on a path table, but it is a true event count that accumulates, so real_additive fits best. </reasoning> <table_type_conflict>false</table_type_conflict> </result>