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\r\nVECTOR BENCHMARK RESULTS:\r\n\r\n
\r\n| Layers | DGGS     | Vector   | Speedup  |\r\n|--------|----------|----------|----------|\r\n| 5      | 0.01s    | 0.4s     | 40x      |\r\n| 10     | 0.015s   | 10s      | 670x     |\r\n| 20     | 0.03s    | 400s     | 16,000x  |\r\n
\r\n\r\nDGGS shows near-linear scaling; vector shows super-linear growth.\r\nThis validates the paper's Figure 6.\r\n\r\nRASTER BENCHMARK RESULTS (100 layers):\r\n\r\n
\r\n| Method              | Time    |\r\n|---------------------|---------|\r\n| Raster (NumPy)      | 0.02s   |\r\n| DGGS Pre-indexed    | 0.01s   | ← Paper's scenario: VALIDATED\r\n| DGGS + H3 loop      | 5.0s    | ← Includes slow indexing\r\n| DGGS + xdggs        | 0.05s   | ← Replication: 100x faster indexing\r\n
\r\n\r\nThe pre-indexed scenario matches the paper's methodology and validates \r\nthe claim of equivalent performance.\r\n
" } ], "https://w3id.org/sciencelive/o/terms/hasLimitationsDescription": [ { "@value": "
\r\n
\r\n- Vector benchmark tested up to 100 layers (paper used 500)\r\n- Raster pre-indexed scenario simulates but doesn't exactly replicate \r\n  Apache Parquet + Polars implementation\r\n- Missing random misalignment (\"jittering\") from original methodology\r\n- Single hardware configuration tested\r\n
\r\n
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