{"id":"W4386324236","doi":"10.3390/jimaging9090179","title":"Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration","year":2023,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Mixture model; Computer science; Point cloud; Multivariate statistics; Algorithm; Divergence (linguistics); Gaussian; Multivariate normal distribution; Transformation (genetics); Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002635492,0.001244145,0.001720664,0.001801386,0.0005348682,0.001115087,0.002537711,0.001840372,0.0009976845],"category_scores_gemma":[0.004724347,0.0008686872,0.002068612,0.00327989,0.001031643,0.002098869,0.002436019,0.001889141,0.0008345784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007779874,"about_ca_system_score_gemma":0.001482294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00601091,"about_ca_topic_score_gemma":0.004453212,"domain_scores_codex":[0.9980526,0.0006071923,0.0001033263,0.0003900753,0.000744245,0.0001027219],"domain_scores_gemma":[0.9990325,0.0003435889,0.0001211581,0.0002000243,0.0002609366,0.0000418458],"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":[0.00011111,0.00005324757,0.0009624981,0.0001201311,0.0001661298,0.0001182709,0.0001094926,0.7685794,0.01099209,0.02089773,0.00161289,0.1962771],"study_design_scores_gemma":[0.000003916511,0.00001111368,0.0001023489,0.000003278715,0.000008431916,0.00003564105,0.000004689963,0.9945509,0.001177913,0.00341308,0.0006757007,0.00001313305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001189707,0.00007511746,0.9983189,0.00004215931,0.00001091997,0.000009860091,0.00001170565,0.000234154,0.0001075447],"genre_scores_gemma":[0.1720027,0.0006099761,0.8245252,0.0001453928,0.00008184612,0.0002027996,0.0003879424,0.0004196848,0.00162444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00601091,"threshold_uncertainty_score":0.01393801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596028167529761,"score_gpt":0.2856476754398706,"score_spread":0.259687393764573,"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."}}