{"id":"W2968518909","doi":"10.1007/978-3-030-23876-6_14","title":"Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation","year":2019,"lang":"en","type":"book-chapter","venue":"Unsupervised and semi-supervised learning","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Unsupervised learning; Segmentation; Pattern recognition (psychology); Image segmentation; Image (mathematics); Statistical learning; Machine learning","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":[0.001283404,0.0009344554,0.001638617,0.00110003,0.0004585117,0.001741348,0.003307502,0.001829594,0.004161139],"category_scores_gemma":[0.002670154,0.001052812,0.001511685,0.002402465,0.001150752,0.002593605,0.001700406,0.002443398,0.002956529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135837,"about_ca_system_score_gemma":0.0009971695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002804975,"about_ca_topic_score_gemma":0.003919739,"domain_scores_codex":[0.9990746,0.0002040628,0.00004824918,0.0002377829,0.0003871559,0.00004815247],"domain_scores_gemma":[0.9991379,0.000425791,0.00005937946,0.0001815889,0.0001663792,0.00002885232],"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":[0.00007491779,0.00006783301,0.0002563331,0.0002700147,0.0001186607,0.0001106555,0.0001155643,0.4061213,0.01201703,0.2420116,0.01420553,0.3246305],"study_design_scores_gemma":[0.000003122236,0.000009504868,0.00008810436,0.00001384852,0.00001195978,0.00005781773,0.000005761025,0.9247103,0.001654656,0.06731001,0.006119769,0.00001506154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002534925,0.0002113572,0.9985941,0.00004832054,0.00001452219,0.000006512749,0.0000432525,0.0002515814,0.0005768868],"genre_scores_gemma":[0.05305178,0.001259686,0.9318295,0.0001786878,0.0001568078,0.0002168466,0.0007404276,0.0007607125,0.01180551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004161139,"threshold_uncertainty_score":0.01392043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676054821758122,"score_gpt":0.2656339888554451,"score_spread":0.2388734406378639,"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."}}