{"id":"W4311633596","doi":"10.3390/rs14236167","title":"Optimization Method of Airborne LiDAR Individual Tree Segmentation Based on Gaussian Mixture Model","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Interreg; Ministry of Science and Technology, Taiwan; Shandong University of Science and Technology; Shandong University","keywords":"Lidar; Computer science; Segmentation; Mixture model; Tree (set theory); Gaussian; Covariance matrix; Artificial intelligence; Covariance; Remote sensing; Cluster analysis; Understory; Pattern recognition (psychology); Environmental science; Canopy; Mathematics; Algorithm; Statistics; Ecology; Geography","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":[0.0007781008,0.0009798883,0.00106784,0.001174626,0.000418151,0.0008400748,0.001216531,0.0007797713,0.001143742],"category_scores_gemma":[0.001124287,0.0006203502,0.001393729,0.001114762,0.0004742104,0.0008607428,0.0006384833,0.0008086262,0.0005124435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013836,"about_ca_system_score_gemma":0.00129176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01489904,"about_ca_topic_score_gemma":0.009925527,"domain_scores_codex":[0.9993469,0.00008274768,0.00003835685,0.0002084811,0.0002335409,0.00008999487],"domain_scores_gemma":[0.9996855,0.00008657912,0.00003613308,0.00002558961,0.0001473423,0.00001890969],"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":[0.0001845814,0.0000600714,0.003376288,0.0001650325,0.0001231402,0.0001072581,0.0002016175,0.5716038,0.02713424,0.00377168,0.002554474,0.390718],"study_design_scores_gemma":[0.000005134986,0.00001488918,0.0004926266,0.000004106735,0.00001306487,0.00002377518,0.0000123644,0.9963639,0.002014376,0.000558538,0.0004888246,0.000008383629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01562596,0.0002630422,0.9826734,0.00006054547,0.00002612496,0.00002862252,0.00004021334,0.0006606359,0.0006214762],"genre_scores_gemma":[0.3855014,0.0005576708,0.6092914,0.0001508377,0.00007756578,0.0001789166,0.0006691715,0.0003655368,0.003207364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01489904,"threshold_uncertainty_score":0.02962464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593194572790907,"score_gpt":0.2605494228898469,"score_spread":0.2446174771619379,"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."}}