I need to identify all numeric columns and compute their distribution statistics in a single query. From the evidence, I can see several numeric columns with their types and ranges. Let me write a comprehensive query to get distribution statistics for all numeric measures. ```json { "action": "tool_call", "reasoning": "I have the column metadata showing numeric columns (DOUBLE type measures). I'll write one SELECT with aggregates for all numeric columns to get min, max, mean, median, and standard deviation in a single query rather than querying each column separately.", "tool": "query_sql", "args": { "sql": "SELECT 'Citations' as column_name, COUNT(*) as total_rows, COUNT(\"Citations\") as non_null_count, MIN(\"Citations\") as min_value, MAX(\"Citations\") as max_value, AVG(\"Citations\") as mean_value, MEDIAN(\"Citations\") as median_value, STDDEV(\"Citations\") as std_dev FROM raw.all_ai_models UNION ALL SELECT 'Parameters', COUNT(*), COUNT(\"Parameters\"), MIN(\"Parameters\"), MAX(\"Parameters\"), AVG(\"Parameters\"), MEDIAN(\"Parameters\"), STDDEV(\"Parameters\") FROM raw.all_ai_models UNION ALL SELECT 'Training compute (FLOP)', COUNT(*), COUNT(\"Training compute (FLOP)\"), MIN(\"Training compute (FLOP)\"), MAX(\"Training compute (FLOP)\"), AVG(\"Training compute (FLOP)\"), MEDIAN(\"Training compute (FLOP)\"), STDDEV(\"Training compute (FLOP)\") FROM raw.all_ai_models UNION ALL SELECT 'Training time (hours)', COUNT(*), COUNT(\"Training time (hours)\"), MIN(\"Training time (hours)\"), MAX(\"Training time (hours)\"), AVG(\"Training time (hours)\"), MEDIAN(\"Training time (hours)\"), STDDEV(\"Training time (hours)\") FROM raw.all_ai_models UNION ALL SELECT 'Epochs', COUNT(*), COUNT(\"Epochs\"), MIN(\"Epochs\"), MAX(\"Epochs\"), AVG(\"Epochs\"), MEDIAN(\"Epochs\"), STDDEV(\"Epochs\") FROM raw.all_ai_models UNION ALL SELECT 'Finetune compute (FLOP)', COUNT(*), COUNT(\"Finetune compute (FLOP)\"), MIN(\"Finetune compute (FLOP)\"), MAX(\"Finetune compute (FLOP)\"), AVG(\"Finetune compute (FLOP)\"), MEDIAN(\"Finetune compute (FLOP)\"), STDDEV(\"Finetune compute (FLOP)\") FROM raw.all_ai_models", "purpose": "Get distribution statistics (min, max, mean, median, std dev) for the first batch of numeric columns: Citations, Parameters, Training compute, Training time, Epochs, and Finetune compute", "source": "raw" } } ```