{"id":"W4408565313","doi":"10.1109/tim.2025.3551981","title":"PV-PASBLS: A Multimodal Point-View Fusion Model Based on Parameter Adaptive Stacked Broad Learning System for 3-D Shape Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Science and Technology Department of Zhejiang Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Fusion; Computer science; Point (geometry); Sensor fusion; Computer vision; Pattern recognition (psychology); Mathematics; Geometry","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":[0.0008497722,0.0009120092,0.0009683141,0.0006801815,0.0004660799,0.001059549,0.002081926,0.00127252,0.003783645],"category_scores_gemma":[0.00158406,0.0006117276,0.001247411,0.0007328242,0.000736889,0.002005649,0.002075873,0.001850253,0.002103313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007169843,"about_ca_system_score_gemma":0.0009707018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004375028,"about_ca_topic_score_gemma":0.003979636,"domain_scores_codex":[0.9994292,0.000100727,0.00002901323,0.0001716665,0.0002117887,0.00005754067],"domain_scores_gemma":[0.9995816,0.00009159361,0.0000503283,0.00007187113,0.0001688464,0.0000357875],"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":[0.0001545337,0.00006383591,0.001159889,0.00007833364,0.00009804563,0.0001312133,0.0001292803,0.6930632,0.01504901,0.009597863,0.003558229,0.2769166],"study_design_scores_gemma":[0.000002711147,0.00002775587,0.0001088083,0.000004329176,0.000006522618,0.00002508941,0.000006768906,0.9956612,0.001178598,0.002146368,0.0008234436,0.000008381504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00444681,0.0001657706,0.9933002,0.00008610695,0.00002871654,0.00003078494,0.00005244937,0.0007264865,0.001162769],"genre_scores_gemma":[0.5397446,0.0006770885,0.4477524,0.0006086709,0.0001188647,0.000424352,0.0008385865,0.0003157483,0.009519754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004375028,"threshold_uncertainty_score":0.01265752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05555673282643434,"score_gpt":0.258635297729881,"score_spread":0.2030785649034466,"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."}}