{
    "action": "final",
    "content": "## Distribution Summary for Numeric Columns\n\nThe dataset contains 14 numeric columns. Here's the distribution summary for each:\n\n### Model Performance Metrics\n- **best_score / Best score (across scorers)**: Ranges from 0.0 to 1.0, with mean 0.43 and median 0.39. Standard deviation of 0.32 indicates substantial variation in model performance across benchmarks.\n- **mean_score**: Nearly identical distribution (0.0–1.0, mean 0.43, median 0.39, stddev 0.32).\n- **stderr**: Ranges 0.0–0.5 with mean 0.033 and median 0.026, showing relatively low uncertainty in score measurements (13 null values).\n\n### Training Scale Metrics\n- **Parameters**: Ranges from 3 billion to 3 trillion parameters (mean 703B, median 358B). High standard deviation (866B) reflects the mix of smaller and massive models. 323 non-null values out of 1,311 rows.\n- **Training compute (FLOP)**: Spans 4.0×10²⁰ to 5.0×10²⁶ FLOPs (mean 4.2×10²⁵, median 5.8×10²⁴). The large spread (stddev 1.0×10²⁶) captures models from moderate to extreme training scales. 267 non-null values.\n- **Training dataset size (datapoints)**: Ranges 320M to 147 trillion datapoints (mean 21 trillion, median 15 trillion). Extremely high variance (stddev 27 trillion). 113 non-null values.\n- **Training compute cost (2023 USD)**: $1.1M to $388M (mean $101M, median $26M, stddev $142M). 60 non-null values.\n\n### Training Configuration\n- **Batch size**: 4M to 64M (mean 48M, median 60M). Only 23 non-null values.\n- **Hardware quantity**: 128 to 200,000 units (mean 44,838, median 6,144). High stddev (68,393) indicates a few extremely large-scale training runs. 50 non-null values.\n- **Hardware utilization**: 0.19 to 0.42 (mean 0.30, median 0.34). 21 non-null values.\n- **Epochs**: 1.0 to 4.0 (mean 1.5, median 1.0). 42 non-null values.\n\n### Other Metrics\n- **Finetune compute (FLOP)**: 1.3×10¹⁸ to 2.5×10²⁴ FLOPs (mean 5.6×10²³, median 1.8×10²³). 20 non-null values.\n- **Citations**: 333 to 24,490 (mean 12,573, median 16,911). 17 non-null values.\n\n### Key Observations\n1. **Performance scores** are well-populated (all 1,311 rows) and show wide variation, with most models scoring below 0.5.\n2. **Training metadata** (parameters, compute, dataset size, cost) has substantial missingness (20–75% null), but when present shows enormous scale variation spanning multiple orders of magnitude.\n3. **Configuration details** (batch size, hardware, epochs) are sparsely populated (96–98% null), limiting analysis of training setups.",
    "charts": []
}