{"id":"W2116578356","doi":"10.1109/iccv.2011.6126330","title":"Recursive MDL via graph cuts: Application to segmentation","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Minimum description length; Segmentation; Maxima and minima; Hierarchy; Image (mathematics); Image segmentation; Algorithm; Representation (politics); Pattern recognition (psychology); Computer science; Mathematics; Artificial intelligence","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":[0.0001909018,0.00008670395,0.00007511034,0.0001435225,0.00005264486,0.00003801732,0.0005213078,0.0000373972,0.0001843219],"category_scores_gemma":[0.0000224609,0.00007918227,0.00002858006,0.0004764742,0.000026634,0.0005270857,0.0001066961,0.00005475373,0.0004107852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004128262,"about_ca_system_score_gemma":0.00001640811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001337033,"about_ca_topic_score_gemma":0.00001086382,"domain_scores_codex":[0.9990376,0.00004229232,0.0002014863,0.0003107785,0.0002551158,0.0001526912],"domain_scores_gemma":[0.9992552,0.00002300557,0.00006888802,0.0003951439,0.000106168,0.0001515455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007443168,0.0001272213,0.0002760632,0.000009079083,0.00001221392,0.000003590824,0.00409008,0.000001120366,0.06252284,0.03150798,0.01802582,0.8834165],"study_design_scores_gemma":[0.0001580117,0.0001688167,0.001252002,0.000008631569,0.000004870692,0.000005918968,0.0001228078,0.001045236,0.9580761,0.0384789,0.0004906526,0.0001880729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002515711,0.000006480482,0.9883314,0.0004098422,0.0001010764,0.0004856807,5.58994e-7,0.0004969765,0.009916355],"genre_scores_gemma":[0.02904138,0.000007509909,0.9665322,0.003828359,0.00002237151,0.0002202801,0.000006850827,0.000006576825,0.0003344852],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8955532,"threshold_uncertainty_score":0.527995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0194820787145562,"score_gpt":0.2760395893236011,"score_spread":0.2565575106090449,"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."}}