I need to systematically examine data quality issues across this dataset. Let me start by profiling key columns for outliers, impossible values, and inconsistencies. {"action": "tool_call", "reasoning": "Profile numeric measures to identify outliers and impossible values. I'll check Parameters, Training compute, Citations, Training time, and other key metrics for extreme values, negative numbers, or impossible ranges.", "tool": "query_sql", "args": {"sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT Model) as distinct_models, COUNT(Model) - COUNT(DISTINCT Model) as duplicate_models, SUM(CASE WHEN Parameters < 0 THEN 1 ELSE 0 END) as negative_params, SUM(CASE WHEN Parameters > 1e15 THEN 1 ELSE 0 END) as extreme_params, MIN(Parameters) as min_params, MAX(Parameters) as max_params, SUM(CASE WHEN \"Training compute (FLOP)\" < 0 THEN 1 ELSE 0 END) as negative_compute, MIN(\"Training compute (FLOP)\") as min_compute, MAX(\"Training compute (FLOP)\") as max_compute, SUM(CASE WHEN Citations < 0 THEN 1 ELSE 0 END) as negative_citations, MAX(Citations) as max_citations, SUM(CASE WHEN \"Training time (hours)\" < 0 THEN 1 ELSE 0 END) as negative_time, MIN(\"Training time (hours)\") as min_time, MAX(\"Training time (hours)\") as max_time, SUM(CASE WHEN Epochs < 0 THEN 1 ELSE 0 END) as negative_epochs, MAX(Epochs) as max_epochs FROM raw.all_ai_models", "purpose": "Check for impossible values (negatives), extreme outliers, and duplicate models", "source": "raw"}}