{"id":"W1998076850","doi":"10.5244/c.22.16","title":"Parameter Selection for Graph Cut Based Image Segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Cut; Segmentation-based object categorization; Segmentation; Image segmentation; Scale-space segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); AdaBoost; Graph; Minimum spanning tree-based segmentation; Feature selection; Classifier (UML); Computer vision; Theoretical computer science","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":[0.0001706193,0.00008570208,0.00008214285,0.0001289039,0.0001309665,0.00006003856,0.0002263629,0.00003807222,0.000119132],"category_scores_gemma":[0.00008106994,0.00007621691,0.00006259136,0.0002955003,0.00004990703,0.0006568992,0.00002248823,0.00005098534,0.0000252829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003672515,"about_ca_system_score_gemma":0.00004786758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002293389,"about_ca_topic_score_gemma":0.000004358075,"domain_scores_codex":[0.9991249,0.00004415559,0.0001846243,0.0002535885,0.0002294653,0.0001632065],"domain_scores_gemma":[0.9994195,0.0001514559,0.00006140654,0.0001729778,0.0001221923,0.00007250626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004882,0.0005035347,0.002731544,0.00009551618,0.00004954679,0.00001323223,0.0008161481,0.00003707896,0.6100122,0.005011,0.1877273,0.192954],"study_design_scores_gemma":[0.0004194502,0.0001385849,0.0003284224,0.000003733568,0.000003422215,0.00001020418,0.000007216348,0.0677821,0.9292687,0.001735655,0.0001918955,0.0001106342],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002271716,0.000003416241,0.9957405,0.0004379771,0.00009491997,0.0004233045,0.000001153521,0.0005705587,0.0004564423],"genre_scores_gemma":[0.02105189,0.000005160204,0.9759052,0.002345841,0.0000262823,0.0001825493,0.00001312775,0.000007025107,0.0004629321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3192565,"threshold_uncertainty_score":0.3108035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02694813217863968,"score_gpt":0.2984128017541642,"score_spread":0.2714646695755245,"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."}}