I'll systematically check for data quality issues across this dataset. Let me start by examining the key measure columns for outliers and impossible values. {"action": "tool_call", "reasoning": "First, I'll profile the numeric measure columns to check for outliers, impossible values (like negative counts), and suspicious ranges. I'll look at all the measure columns identified in the card.", "tool": "query_sql", "source": "raw", "args": {"sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT model) as distinct_models, COUNT(\"Best score (across scorers)\") as non_null_best_score, MIN(\"Best score (across scorers)\") as min_best_score, MAX(\"Best score (across scorers)\") as max_best_score, AVG(\"Best score (across scorers)\") as avg_best_score, MEDIAN(\"Best score (across scorers)\") as median_best_score, STDDEV(\"Best score (across scorers)\") as stddev_best_score, COUNT(\"Training compute (FLOP)\") as non_null_training_compute, MIN(\"Training compute (FLOP)\") as min_training_compute, MAX(\"Training compute (FLOP)\") as max_training_compute, COUNT(Parameters) as non_null_parameters, MIN(Parameters) as min_parameters, MAX(Parameters) as max_parameters, COUNT(\"Hardware quantity\") as non_null_hardware_qty, MIN(\"Hardware quantity\") as min_hardware_qty, MAX(\"Hardware quantity\") as max_hardware_qty, COUNT(\"Training compute cost (2023 USD)\") as non_null_training_cost, MIN(\"Training compute cost (2023 USD)\") as min_training_cost, MAX(\"Training compute cost (2023 USD)\") as max_training_cost FROM raw.epoch_ai_benchmarks", "purpose": "Profile numeric measures to identify outliers, impossible values, and data ranges"}}