{"id":"W2953163286","doi":"10.48550/arxiv.1704.08908","title":"Unbiased Shape Compactness for Segmentation","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Compact space; Pairwise comparison; Regularization (linguistics); Cut; Computer science; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Mathematical optimization; Image segmentation","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":[0.00148849,0.001047749,0.0009369008,0.0012496,0.0004962998,0.001647272,0.001699475,0.001976299,0.002671895],"category_scores_gemma":[0.006374424,0.0008730361,0.000846304,0.0009754433,0.001651825,0.002913875,0.002709384,0.001707806,0.001602827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008890417,"about_ca_system_score_gemma":0.001158067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241849,"about_ca_topic_score_gemma":0.002231988,"domain_scores_codex":[0.9990392,0.0002352263,0.00004431729,0.0002236475,0.0003931051,0.00006452471],"domain_scores_gemma":[0.9983971,0.0006429408,0.0002383702,0.0004226138,0.000219254,0.0000797705],"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":[0.0001829242,0.00009022167,0.001573006,0.0003084284,0.00007802237,0.0002220306,0.000281007,0.5606172,0.06980547,0.1063109,0.006874205,0.2536566],"study_design_scores_gemma":[0.00001173848,0.00004055855,0.0004027586,0.0000316093,0.00001116306,0.0002310265,0.00002806334,0.9399798,0.01281907,0.04231132,0.004113975,0.00001897559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004895788,0.0001205492,0.9931669,0.0001746197,0.00001316128,0.00001832377,0.00005097039,0.0003404421,0.001219329],"genre_scores_gemma":[0.2506711,0.0005611073,0.7407625,0.0005547676,0.0001348342,0.0002029812,0.0007451563,0.0009402162,0.005427483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002671895,"threshold_uncertainty_score":0.008938372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1341992919556988,"score_gpt":0.2194967090870681,"score_spread":0.08529741713136926,"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."}}