{"id":"W1519712624","doi":"10.1007/978-3-540-89639-5_26","title":"A Continuous Labeling for Multiphase Graph Cut Image Partitioning","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"General Electric (Canada); Institut National de la Recherche Scientifique","funders":"","keywords":"Cut; Graph partition; Computer science; Iterated function; Piecewise; Graph; Maximum cut; Partition (number theory); Minimum cut; Segmentation; Image (mathematics); Image segmentation; Artificial intelligence; Algorithm; Mathematics; Theoretical computer science; Combinatorics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008486398,0.0004619195,0.0005312534,0.0007746241,0.0004140893,0.0005262327,0.002270298,0.0002533687,0.00002358346],"category_scores_gemma":[0.0003828967,0.00044679,0.0001837718,0.0004760387,0.0009682419,0.0008852551,0.0006374423,0.0005982351,0.00002312918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186433,"about_ca_system_score_gemma":0.0003848347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000192186,"about_ca_topic_score_gemma":0.00002159019,"domain_scores_codex":[0.996292,0.00003456385,0.0006656409,0.001405912,0.0009101566,0.0006917589],"domain_scores_gemma":[0.9972086,0.0007196767,0.0003650931,0.0009828716,0.0004822501,0.0002415189],"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":[0.00000872688,0.0000599951,0.00001290431,0.00006798313,0.00001553907,0.0001955552,0.001054213,0.001631396,0.002955066,0.001951911,0.0006381928,0.9914085],"study_design_scores_gemma":[0.001817725,0.0006480179,0.00001421407,0.001325356,0.00002676694,0.0003003797,6.826024e-7,0.7362651,0.129565,0.1246059,0.003586943,0.001843926],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001651563,0.0004014556,0.9963391,0.0004324678,0.0008839746,0.0008526297,0.00001255644,0.0004801606,0.0005811377],"genre_scores_gemma":[0.002156807,0.0001467307,0.9946154,0.002427817,0.00030615,0.00006751599,0.00001548098,0.00003662145,0.0002274132],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9895646,"threshold_uncertainty_score":0.9997984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02123429327167649,"score_gpt":0.2865642261312774,"score_spread":0.2653299328596009,"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."}}