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- dggs-replication-2026 hasDeviationDescription "1. SCALE: The original paper tested up to 500 vector layers; our default configuration tests [5, 10, 20, 50, 100] layers but supports scaling to 500. 2. RASTER GENERATION: The paper used NLMpy mid-point displacement algorithm. Our implementation uses NLMpy when available, with Gaussian filter fallback. 3. RANDOM MISALIGNMENT: The paper mentions "jittering the origin point by up to one pixel" for raster alignment - this feature is not implemented in our reproduction. 4. ADDITIONAL COMPARISON: We added xdggs as an alternative DGGS implementation not present in the original study, extending the work from pure reproduction to include replication with different tools. 5. PRE-INDEXED SCENARIO: The paper's raster benchmark used pre-indexed data in Apache Parquet queried with Polars. Our benchmark includes both on-the-fly indexing and pre-indexed scenarios to enable direct comparison." assertion.