{"id":"W2112475072","doi":"10.1109/tpami.2009.120","title":"Order-Preserving Moves for Graph-Cut-Based Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Cut; Computer science; Pixel; Segmentation; Graph; Algorithm; Artificial intelligence; Image segmentation; 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":[0.0001775554,0.0001952213,0.0002543732,0.000530112,0.000234167,0.0001584478,0.0004414888,0.00005980618,0.00004057417],"category_scores_gemma":[0.00001379878,0.0001736384,0.0002247408,0.001485771,0.00003390569,0.0004322018,0.000003008978,0.0001438505,0.000001733217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002007006,"about_ca_system_score_gemma":0.00001682845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001132449,"about_ca_topic_score_gemma":0.0001124557,"domain_scores_codex":[0.9987742,0.00004180127,0.0003048216,0.0004712137,0.0001894867,0.0002184804],"domain_scores_gemma":[0.9990289,0.000148064,0.00009987829,0.0004675362,0.0001661675,0.00008951797],"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":[0.00001333168,0.0000962834,0.00002516602,0.000008341537,0.00007100186,0.000001589448,0.00004425555,0.3373515,0.0002562689,0.00009250973,0.000007646535,0.6620321],"study_design_scores_gemma":[0.00007034225,0.0002204773,0.00005636323,0.00001538357,0.0001202815,0.000001168797,0.000006734927,0.6689938,0.3286333,0.001669056,0.00004504489,0.000167991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00007320771,0.0001076795,0.9984875,0.0007991366,0.0000553418,0.0002093448,0.00002209487,0.0002030122,0.0000426249],"genre_scores_gemma":[0.8491186,0.0003229717,0.149306,0.00112753,0.00001100593,0.00002582627,0.000007683639,0.000007961122,0.00007240519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8491816,"threshold_uncertainty_score":0.708077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0214718095917776,"score_gpt":0.2975552386376602,"score_spread":0.2760834290458826,"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."}}