{"id":"W4387828784","doi":"10.1109/igarss52108.2023.10282100","title":"Investigating the Impact of Point Cloud Density on Semantic Segmentation Performance Using Virtual Lidar in Boreal Forest","year":2023,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nature","keywords":"Lidar; Point cloud; Remote sensing; Segmentation; Computer science; Taiga; Artificial intelligence; Point (geometry); Mathematics; Geography; Forestry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002309727,0.00137241,0.0006698996,0.001323495,0.0006412428,0.00109974,0.0009974232,0.0008852257,0.0005677763],"category_scores_gemma":[0.005668392,0.0004135173,0.0007981133,0.0008133468,0.000675878,0.001883277,0.001058631,0.0005811576,0.0002565046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170711,"about_ca_system_score_gemma":0.0007954418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06086352,"about_ca_topic_score_gemma":0.07269411,"domain_scores_codex":[0.9989557,0.0001988306,0.00009007528,0.0003155341,0.0002540657,0.0001858912],"domain_scores_gemma":[0.9973748,0.001444265,0.000194402,0.0003432049,0.000446116,0.0001972458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00170852,0.0004854817,0.1311773,0.0002223908,0.0004417955,0.0002614478,0.0004429954,0.6380683,0.02822213,0.0007775953,0.002353929,0.1958381],"study_design_scores_gemma":[0.00003409737,0.0003335051,0.03613139,0.00002262979,0.0000746209,0.000121467,0.0003143863,0.9471684,0.01473749,0.0004073298,0.0006200225,0.00003477449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837176,0.0003608223,0.01326432,0.00009050683,0.00003119568,0.00003733631,0.0003645471,0.001057324,0.001076392],"genre_scores_gemma":[0.9804047,0.00009398913,0.01758354,0.00004637732,0.000008851596,0.00001373084,0.001450695,0.00007831874,0.0003198043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06086352,"threshold_uncertainty_score":0.1210185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225768298898938,"score_gpt":0.2765757075859969,"score_spread":0.2539988776961031,"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."}}