{"id":"W4410328221","doi":"10.20944/preprints202505.0798.v1","title":"Large-Scale Point Cloud Semantic Segmentation with Density-Based Grid Decimation","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China University of Geosciences; National Natural Science Foundation of China","keywords":"Decimation; Point cloud; Scale (ratio); Segmentation; Computer science; Grid; Cloud computing; Point (geometry); Artificial intelligence; Data mining; Geography; Computer vision; Cartography; Mathematics; Geodesy; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006140202,0.0003398176,0.0003206586,0.0001004863,0.0002749452,0.00004570698,0.0004056006,0.0002337597,0.0008037467],"category_scores_gemma":[0.00004865951,0.0003295134,0.000135201,0.000293061,0.0001386026,0.00009474099,0.000894989,0.0005579829,0.002324526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004493363,"about_ca_system_score_gemma":0.00009397179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006923514,"about_ca_topic_score_gemma":0.0005887066,"domain_scores_codex":[0.9974837,0.0001643642,0.0004283444,0.001059857,0.0005126362,0.0003510757],"domain_scores_gemma":[0.9980983,0.00007119151,0.00031537,0.001337837,0.00005104526,0.0001262959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001362181,0.0006212387,0.8546218,0.0003009127,0.0001148691,0.00001212026,0.003693607,0.1154315,0.02161574,0.0001091199,0.001244356,0.002098563],"study_design_scores_gemma":[0.001080127,0.00003361186,0.7802924,0.0004716403,0.000295442,0.00001399227,0.0005079948,0.04105243,0.1667355,0.002461586,0.006144244,0.0009110782],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8983029,0.000007264403,0.08407996,0.0009233651,0.0003816309,0.001004396,0.00003586312,0.000231252,0.01503334],"genre_scores_gemma":[0.9867764,0.00001549043,0.01088035,0.0004413723,0.0001153932,0.00005664185,0.0003302133,0.00002886102,0.001355349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1451197,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03172102983349417,"score_gpt":0.2941898139903013,"score_spread":0.2624687841568071,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}