```xml <result> <column_name>Post-training compute (FLOP)</column_name> <family>additive</family> <subtype>real_additive</subtype> <confidence>0.85</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</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>false</multiplicative_partner_derivable> <reasoning> Table type is DIMENSION with no sequence axis, making multiplicative highly implausible. The column name "Post-training compute (FLOP)" indicates an absolute extensive quantity of computational work (floating-point operations), which is a classic real_additive measure. The unit FLOP (floating-point operations) is an absolute count, not a ratio. The single observed value (9.4e+22) is a very large magnitude far from 1.0, consistent with an extensive quantity that accumulates. Despite 99.97% null fraction limiting statistical signals, the naming and unit semantics strongly support real_additive classification. </reasoning> <table_type_conflict>false</table_type_conflict> </result> ```