{"id":"W2296473622","doi":"10.1109/icip.2015.7351636","title":"Ranked k-means clustering for terahertz image segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Terahertz technology and applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cluster analysis; Initialization; Terahertz radiation; Sample (material); Computer science; Artificial intelligence; Image segmentation; Segmentation; Pattern recognition (psychology); Set (abstract data type); Fuzzy clustering; Sampling (signal processing); Correlation clustering; Image (mathematics); k-means clustering; Data mining; Computer vision; Physics; Optics","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":[0.001008314,0.0009433028,0.0011355,0.001937902,0.001109715,0.001194174,0.001527197,0.001220316,0.002257773],"category_scores_gemma":[0.003306729,0.0006741275,0.001067349,0.001924528,0.0007266793,0.001424547,0.0008329541,0.001274245,0.001986304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371924,"about_ca_system_score_gemma":0.00140056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005932166,"about_ca_topic_score_gemma":0.008945573,"domain_scores_codex":[0.9988575,0.0002737285,0.00006675204,0.0002534136,0.0004499681,0.00009863206],"domain_scores_gemma":[0.9986112,0.0003415365,0.000144654,0.0003150856,0.0005447385,0.00004278595],"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":[0.000590392,0.0001386342,0.001633416,0.000461884,0.0002051917,0.0001532072,0.0004944802,0.3479262,0.09714746,0.02776841,0.007125407,0.5163553],"study_design_scores_gemma":[0.00002285706,0.00007038672,0.001312166,0.00002255177,0.00002628856,0.0001253922,0.0001057277,0.9329478,0.04505751,0.01410292,0.00611823,0.00008820411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006259449,0.0001973317,0.9916373,0.00005708118,0.00002366964,0.00004785271,0.00006913256,0.0009951836,0.0007129683],"genre_scores_gemma":[0.1028607,0.000271069,0.8945168,0.00007291188,0.00002883076,0.0001402548,0.0003638823,0.0003015503,0.001443963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005932166,"threshold_uncertainty_score":0.01179522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024446101115547,"score_gpt":0.2564548439980737,"score_spread":0.2362103829869182,"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."}}