```xml <result> <column_name>s_boxer_age</column_name> <family>additive</family> <subtype>intensive_additive</subtype> <confidence>0.95</confidence> <requires_user_confirmation>false</requires_user_confirmation> <sum_meaningful>false</sum_meaningful> <signed>false</signed> <reducers> <primary_reducer>mean</primary_reducer> <valid_reducers>mean, min, max, median, distribution</valid_reducers> <excluded_reducers>sum (summing ages across entities is meaningless), product (not a ratio)</excluded_reducers> <average_semantics>arithmetic_mean</average_semantics> </reducers> <inner_outer_measure> <inner_measure_reducer>min</inner_measure_reducer> <outer_measure_reducer>max</outer_measure_reducer> </inner_outer_measure> <multiplicative_partner_derivable>false</multiplicative_partner_derivable> <reasoning> Table type is DIMENSION with no sequence axis, making multiplicative highly implausible. The column name "s_boxer_age" clearly indicates an age measure, which is a classic intensive level quantity. Age values (26-47) are absolute levels centered away from both 0 and 1, with no implicit division. Summing ages across boxers is meaningless; averaging is the natural aggregation. All signals strongly support intensive_additive with high confidence. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```