```json { "action": "tool_call", "reasoning": "The question asks for distribution statistics (min, max, mean, median, spread) across all numeric columns. The card lists many MEASURES columns. I'll write ONE SQL query that computes all these aggregates for every numeric column in the main table, rather than querying columns one at a time. I'll use DuckDB's aggregate functions: MIN, MAX, AVG, MEDIAN, STDDEV for spread.", "tool": "query_sql", "args": { "sql": "SELECT 'Batch size' AS column_name, MIN(\"Batch size\") AS min_val, MAX(\"Batch size\") AS max_val, AVG(\"Batch size\") AS mean_val, MEDIAN(\"Batch size\") AS median_val, STDDEV(\"Batch size\") AS stddev_val FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Best score (across scorers)', MIN(\"Best score (across scorers)\"), MAX(\"Best score (across scorers)\"), AVG(\"Best score (across scorers)\"), MEDIAN(\"Best score (across scorers)\"), STDDEV(\"Best score (across scorers)\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Citations', MIN(\"Citations\"), MAX(\"Citations\"), AVG(\"Citations\"), MEDIAN(\"Citations\"), STDDEV(\"Citations\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT '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.epoch_ai_benchmarks UNION ALL SELECT 'Hardware quantity', MIN(\"Hardware quantity\"), MAX(\"Hardware quantity\"), AVG(\"Hardware quantity\"), MEDIAN(\"Hardware quantity\"), STDDEV(\"Hardware quantity\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Hardware utilization', MIN(\"Hardware utilization\"), MAX(\"Hardware utilization\"), AVG(\"Hardware utilization\"), MEDIAN(\"Hardware utilization\"), STDDEV(\"Hardware utilization\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Parameters', MIN(\"Parameters\"), MAX(\"Parameters\"), AVG(\"Parameters\"), MEDIAN(\"Parameters\"), STDDEV(\"Parameters\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT '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.epoch_ai_benchmarks UNION ALL SELECT 'Training compute cost (2023 USD)', MIN(\"Training compute cost (2023 USD)\"), MAX(\"Training compute cost (2023 USD)\"), AVG(\"Training compute cost (2023 USD)\"), MEDIAN(\"Training compute cost (2023 USD)\"), STDDEV(\"Training compute cost (2023 USD)\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Training dataset size (datapoints)', MIN(\"Training dataset size (datapoints)\"), MAX(\"Training dataset size (datapoints)\"), AVG(\"Training dataset size (datapoints)\"), MEDIAN(\"Training dataset size (datapoints)\"), STDDEV(\"Training dataset size (datapoints)\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'best_score', MIN(\"best_score\"), MAX(\"best_score\"), AVG(\"best_score\"), MEDIAN(\"best_score\"), STDDEV(\"best_score\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'billable_input_tokens', MIN(\"billable_input_tokens\"), MAX(\"billable_input_tokens\"), AVG(\"billable_input_tokens\"), MEDIAN(\"billable_input_tokens\"), STDDEV(\"billable_input_tokens\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'billable_output_tokens', MIN(\"billable_output_tokens\"), MAX(\"billable_output_tokens\"), AVG(\"billable_output_tokens\"), MEDIAN(\"billable_output_tokens\"), STDDEV(\"billable_output_tokens\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'cache_read_tokens', MIN(\"cache_read_tokens\"), MAX(\"cache_read_tokens\"), AVG(\"cache_read_tokens\"), MEDIAN(\"cache_read_tokens\"), STDDEV(\"cache_read_tokens\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'cache_write_tokens', MIN(\"cache_write_tokens\"), MAX(\"cache_write_tokens\"), AVG(\"cache_write_tokens\"), MEDIAN(\"cache_write_tokens\"), STDDEV(\"cache_write_tokens\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'mean_score', MIN(\"mean_score\"), MAX(\"mean_score\"), AVG(\"mean_score\"), MEDIAN(\"mean_score\"), STDDEV(\"mean_score\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'stderr', MIN(\"stderr\"), MAX(\"stderr\"), AVG(\"stderr\"), MEDIAN(\"stderr\"), STDDEV(\"stderr\") FROM raw.epoch_ai_benchmarks ORDER BY column_name", "purpose": "Compute distribution statistics (min, max, mean, median, stddev) for all numeric columns in the dataset", "source": "raw" } } ```