{"id":"W4393123423","doi":"10.1016/j.jag.2024.103791","title":"Dynamic clustering transformer network for point cloud segmentation","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Point cloud; Cluster analysis; Encoder; Segmentation; Feature learning; Artificial intelligence; Feature (linguistics); Data mining; Pattern recognition (psychology); Computer vision","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003034503,0.00008896738,0.0001059471,0.0001686355,0.00004181191,0.0001737669,0.00007066476,0.00004555669,0.00002091611],"category_scores_gemma":[0.000005608936,0.00008223039,0.00007722019,0.0001020014,0.00000801688,0.0006630981,0.0000043739,0.00009666843,0.00000705745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004410739,"about_ca_system_score_gemma":0.00001753123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001271135,"about_ca_topic_score_gemma":0.0000117117,"domain_scores_codex":[0.9991603,0.000003036833,0.0004728556,0.00005334549,0.0002139533,0.00009650613],"domain_scores_gemma":[0.9996642,0.00004078264,0.00007903634,0.00003253419,0.0001471721,0.00003631304],"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":[0.00004708027,0.000003560407,0.00001654132,0.00007927461,0.0001536541,6.455971e-7,0.0009818991,0.8409632,0.001131736,0.001112816,0.0005959271,0.1549137],"study_design_scores_gemma":[0.0003718242,0.00002167862,0.0003539532,0.00008706291,0.00003446344,0.00001422395,0.000202177,0.9888217,0.0004822148,0.001617727,0.007905745,0.00008715878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1132726,0.0002054083,0.8842584,0.0004968896,0.001047688,0.0001111387,0.00001319306,0.00006705931,0.0005276671],"genre_scores_gemma":[0.9790508,0.0004234886,0.019796,0.0002417105,0.0002826296,0.000009159059,0.0001385012,0.00001345583,0.00004424595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8657782,"threshold_uncertainty_score":0.3353258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009767821662293098,"score_gpt":0.2310561860150949,"score_spread":0.2212883643528018,"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."}}