{"id":"W3214347160","doi":"10.1016/j.ifacol.2021.08.023","title":"Reducing Noises in Digital Surface Inspection Using a Data Clustering Approach","year":2021,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Cluster analysis; Computer science; Noise (video); Metrology; Noise reduction; Process (computing); Point cloud; Data mining; Point (geometry); Task (project management); Artificial intelligence; Engineering; Systems engineering; Mathematics; Image (mathematics)","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":[0.000156803,0.0001749765,0.000225864,0.00008645649,0.00005990614,0.00004654228,0.0001791314,0.00009993876,0.000009104213],"category_scores_gemma":[0.0001026115,0.0001935367,0.00003267432,0.000339977,0.00003008308,0.0006372096,0.0001360725,0.0002565599,0.00000252976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001534023,"about_ca_system_score_gemma":0.0000321718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002600686,"about_ca_topic_score_gemma":0.00009222117,"domain_scores_codex":[0.9989654,0.00001408473,0.0002371511,0.0003544759,0.0001412193,0.0002876308],"domain_scores_gemma":[0.9994214,0.00002418304,0.00003141515,0.0004341108,0.00003955469,0.00004937243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003311087,0.0001754075,0.007263944,0.0001926801,0.00007724837,0.00007940088,0.0004860257,0.3327729,0.6426347,0.00001742705,0.00001506921,0.01625211],"study_design_scores_gemma":[0.0005996619,0.00002289752,0.0007340864,0.000139254,0.00002956899,0.00009391127,0.0006396334,0.973919,0.02292366,0.00003335367,0.0004653885,0.0003995408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8096863,0.00131502,0.1844652,0.00004126003,0.0002749759,0.0001470381,0.00005115302,0.0007824016,0.003236648],"genre_scores_gemma":[0.5561794,0.0001065313,0.4433117,0.00001776363,0.0001152116,0.000002485092,0.0001822697,0.000033022,0.00005162379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6411462,"threshold_uncertainty_score":0.7892199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0668007001044541,"score_gpt":0.2853990344102845,"score_spread":0.2185983343058304,"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."}}