{"id":"W2001014393","doi":"10.1016/j.isprsjprs.2014.04.015","title":"Classification of airborne laser scanning data using JointBoost","year":2014,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":219,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Lidar; Point cloud; Computer science; Classifier (UML); Terrain; Artificial intelligence; Segmentation; Pattern recognition (psychology); Remote sensing; Contextual image classification; Feature extraction; Feature selection; Data mining; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001502324,0.001669025,0.001747802,0.00394551,0.0009969216,0.001266419,0.001745659,0.001449734,0.002280495],"category_scores_gemma":[0.001130347,0.0005767118,0.001814974,0.003763792,0.0005604666,0.001267453,0.001098372,0.001144483,0.001963565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006182987,"about_ca_system_score_gemma":0.001484579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01289431,"about_ca_topic_score_gemma":0.0161798,"domain_scores_codex":[0.9987491,0.0001480685,0.00009573124,0.0003177125,0.0003764853,0.0003128506],"domain_scores_gemma":[0.9991008,0.0002365698,0.00007725592,0.0001219523,0.000405232,0.00005825724],"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":[0.0005825892,0.0008637002,0.007713804,0.0001633015,0.000237032,0.0001177518,0.0001127683,0.09568329,0.02978404,0.0006875152,0.006804149,0.85725],"study_design_scores_gemma":[0.00002089985,0.0001382912,0.005380222,0.00001899752,0.00006338151,0.00007650178,0.00008820631,0.9817031,0.009531739,0.001087058,0.001866977,0.00002460435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3151785,0.001227264,0.6721172,0.0002864209,0.0004105731,0.0004097691,0.001368813,0.006098675,0.0029028],"genre_scores_gemma":[0.71622,0.0003643079,0.2704496,0.0001452374,0.0001521854,0.0003155644,0.005329648,0.0003059739,0.006717495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01289431,"threshold_uncertainty_score":0.02563852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03540422244379766,"score_gpt":0.2821903730649533,"score_spread":0.2467861506211556,"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."}}