```json { "action": "tool_call", "reasoning": "I have the column metadata showing which columns are numeric (DOUBLE type). The token columns (billable_input_tokens, billable_output_tokens, cache_read_tokens, cache_write_tokens) are VARCHAR with only empty strings, so I'll exclude them. I need to compute distribution statistics (min, max, mean, median, stddev) for all numeric columns in one query. I'll query the actual numeric columns: Batch size, Citations, Finetune compute (FLOP), Hardware quantity, Hardware utilization, Parameters, Training compute (FLOP), Training compute cost (2023 USD), Training dataset size (datapoints), best_score, mean_score, stderr, Epochs, and Best score (across scorers).", "tool": "query_sql", "args": { "sql": "SELECT 'Batch size' AS column_name, COUNT(*) AS total_rows, COUNT(\"Batch size\") AS non_null, 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 'Citations', COUNT(*), COUNT(\"Citations\"), MIN(\"Citations\"), MAX(\"Citations\"), AVG(\"Citations\"), MEDIAN(\"Citations\"), STDDEV(\"Citations\") FROM raw.epoch_ai_benchmarks 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.epoch_ai_benchmarks UNION ALL SELECT 'Hardware quantity', COUNT(*), COUNT(\"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', COUNT(*), COUNT(\"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', COUNT(*), COUNT(\"Parameters\"), MIN(\"Parameters\"), MAX(\"Parameters\"), AVG(\"Parameters\"), MEDIAN(\"Parameters\"), STDDEV(\"Parameters\") FROM raw.epoch_ai_benchmarks 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.epoch_ai_benchmarks UNION ALL SELECT 'Training compute cost (2023 USD)', COUNT(*), COUNT(\"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)', COUNT(*), COUNT(\"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', COUNT(*), COUNT(\"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 'mean_score', COUNT(*), COUNT(\"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', COUNT(*), COUNT(\"stderr\"), MIN(\"stderr\"), MAX(\"stderr\"), AVG(\"stderr\"), MEDIAN(\"stderr\"), STDDEV(\"stderr\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Epochs', COUNT(*), COUNT(\"Epochs\"), MIN(\"Epochs\"), MAX(\"Epochs\"), AVG(\"Epochs\"), MEDIAN(\"Epochs\"), STDDEV(\"Epochs\") FROM raw.epoch_ai_benchmarks UNION ALL SELECT 'Best score (across scorers)', COUNT(*), COUNT(\"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", "purpose": "Compute distribution statistics (min, max, mean, median, stddev) for all numeric columns in the dataset" } } ```