{"id":"W2146191836","doi":"10.1109/igarss.1990.688832","title":"Versatile And Efficient Hierarchical Clustering For Picture Segmentation","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence; Segmentation; Image segmentation; Hierarchical clustering; Pattern recognition (psychology); Computer vision","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":[0.0004351336,0.0008889075,0.001048468,0.002130022,0.0009322601,0.001217265,0.002398843,0.001018918,0.007406312],"category_scores_gemma":[0.001793343,0.0008569065,0.0009402076,0.002731785,0.0004890448,0.001529108,0.001728806,0.0009804085,0.004570806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007388266,"about_ca_system_score_gemma":0.0009796371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005323098,"about_ca_topic_score_gemma":0.009677567,"domain_scores_codex":[0.9992074,0.0001169445,0.00004469929,0.0001521144,0.0003784374,0.0001004357],"domain_scores_gemma":[0.9992166,0.0001467603,0.00004447069,0.0003098064,0.0002364093,0.00004598677],"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":[0.0002486446,0.00008793282,0.0003906275,0.0002191433,0.0000735457,0.00009383827,0.0001350593,0.03673394,0.1671806,0.007727223,0.01124911,0.7758603],"study_design_scores_gemma":[0.00005155287,0.0001002292,0.001332979,0.00002372813,0.00004941666,0.0003869943,0.0001121644,0.8562034,0.1108725,0.0160065,0.01479217,0.00006841406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00511978,0.0001980638,0.9900574,0.00004883558,0.00002333835,0.00007552133,0.0001981443,0.003087568,0.001191398],"genre_scores_gemma":[0.05826012,0.0001782571,0.937441,0.00004416542,0.00002424366,0.00009848808,0.0007355293,0.000585986,0.002632271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007406312,"threshold_uncertainty_score":0.02477664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01371950722749995,"score_gpt":0.2658803174803845,"score_spread":0.2521608102528846,"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."}}