{"id":"W4407574641","doi":"10.1109/tase.2025.3542076","title":"Automatic Point Cloud Clustering for Surface Defect Diagnosis","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Program of Zhejiang Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cluster analysis; Cloud computing; Point cloud; Computer science; Point (geometry); Artificial intelligence; Data mining; Mathematics; Geometry; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006659808,0.0001619455,0.0001796337,0.0004526411,0.000314053,0.0001763454,0.0001051303,0.00009168431,0.00001293225],"category_scores_gemma":[0.00004020828,0.0001645913,0.00007951921,0.0009631202,0.00003637154,0.0004184128,0.00000171847,0.0001349431,0.00001000303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002204496,"about_ca_system_score_gemma":0.00004194861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001181709,"about_ca_topic_score_gemma":0.000005484295,"domain_scores_codex":[0.9989679,0.000009827418,0.0002935708,0.000227837,0.0002313001,0.0002696065],"domain_scores_gemma":[0.9994326,0.0001941601,0.00002524506,0.000178077,0.00009302914,0.00007686246],"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":[0.000004107845,0.0000111656,0.000004849523,0.0001855918,0.00002466035,3.14382e-7,0.0001682577,0.9210535,0.01746542,0.00007079542,0.0002104057,0.06080099],"study_design_scores_gemma":[0.0003199717,0.00004117683,0.0001691874,0.0001785153,0.00002148364,0.000005089898,0.00006755967,0.9331979,0.06491196,0.00001450901,0.0009175593,0.0001550684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3157144,0.00003972588,0.6808352,0.00004075074,0.002187084,0.0003331313,0.000006415215,0.0006895487,0.0001536964],"genre_scores_gemma":[0.9976134,0.00002418163,0.002081179,0.00002962196,0.00004078692,0.0001534904,4.21924e-7,0.0000182452,0.00003870345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.681899,"threshold_uncertainty_score":0.6711839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463093853041155,"score_gpt":0.2473186272512891,"score_spread":0.2326876887208776,"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."}}