{"id":"W4406291830","doi":"10.1016/j.eswa.2025.126432","title":"MTCloud: Multi-type convolutional linkage network for point cloud instance segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Xiamen Southern Oceanographic Center; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Linkage (software); Point cloud; Segmentation; Type (biology); Artificial intelligence; Convolutional neural network; Cloud computing; Data mining; Pattern recognition (psychology); Operating system","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":[0.0006492495,0.001989134,0.001163364,0.002036896,0.0009002677,0.001747402,0.003844742,0.002510827,0.01159229],"category_scores_gemma":[0.002032905,0.001290285,0.001455158,0.002220728,0.0004555169,0.002061032,0.002806504,0.002365594,0.006044593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001549485,"about_ca_system_score_gemma":0.001714513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0227772,"about_ca_topic_score_gemma":0.04186301,"domain_scores_codex":[0.9995654,0.0000357346,0.00001786341,0.0001786488,0.0001285832,0.00007377054],"domain_scores_gemma":[0.999526,0.00009662363,0.00004081792,0.0001432473,0.0001446554,0.00004860182],"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":[0.0006494251,0.0003381525,0.002995622,0.0004005962,0.0004849969,0.0003295034,0.0001394513,0.1787253,0.02315701,0.008602756,0.08810776,0.6960695],"study_design_scores_gemma":[0.00002096033,0.00003549005,0.0003906419,0.00001688932,0.00002399751,0.00006214161,0.00001558703,0.9830625,0.008151692,0.0035416,0.00466324,0.00001522756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01888176,0.000597381,0.9066951,0.0003539443,0.0002026929,0.0002478347,0.006594446,0.06318551,0.003241299],"genre_scores_gemma":[0.19339,0.0005759035,0.7641782,0.0005505743,0.0001185382,0.000468777,0.02208775,0.003681539,0.0149487],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0227772,"threshold_uncertainty_score":0.04528922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406384592608546,"score_gpt":0.261084336711436,"score_spread":0.2470204907853505,"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."}}