{"id":"W4385422845","doi":"10.3390/rs15153787","title":"Classification of Large-Scale Mobile Laser Scanning Data in Urban Area with LightGBM","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Point cloud; Artificial intelligence; Classifier (UML); Boosting (machine learning); Feature extraction; Gradient boosting; Scale (ratio); Machine learning; Pattern recognition (psychology); Data mining; Random forest; Geography; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0004174069,0.0001119678,0.0001551851,0.0000901148,0.000108522,0.00002151642,0.000191006,0.00006140851,0.00001936671],"category_scores_gemma":[0.00002824305,0.0001006763,0.00002137846,0.0009881448,0.0001047119,0.0001346553,0.0001771228,0.0001314622,0.0001675403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000768185,"about_ca_system_score_gemma":0.00001732941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004640445,"about_ca_topic_score_gemma":0.0008176342,"domain_scores_codex":[0.9987444,0.0000485798,0.0002309258,0.0004269524,0.0002696846,0.0002795176],"domain_scores_gemma":[0.9988606,0.00006300648,0.0001082153,0.0008938967,0.00001300363,0.00006126898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009289478,0.000184754,0.03220284,0.00007590888,0.00004182491,0.0000786009,0.01025736,0.04437296,0.4257583,0.00002330034,0.01628408,0.4706272],"study_design_scores_gemma":[0.0002540481,0.00002330319,0.04216672,0.0001132755,0.00001397761,0.00001568846,0.00102719,0.9364036,0.005067717,0.00006641218,0.01467916,0.0001688491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838493,0.00001114119,0.005950536,0.0002517963,0.00003512878,0.0002149,0.00001079558,0.00009897556,0.009577377],"genre_scores_gemma":[0.9850304,0.00001260484,0.01419871,0.00003712949,0.00003073686,3.065546e-8,0.0001131719,0.00002332065,0.0005539166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8920307,"threshold_uncertainty_score":0.410546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02500582306288676,"score_gpt":0.2679288242147366,"score_spread":0.2429230011518499,"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."}}