{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006542779,0.0002455067,0.0002706603,0.0001279348,0.0001410137,0.00008776438,0.0004737129,0.0002276952,0.00002481159],"category_scores_gemma":[0.00001360982,0.0003090822,0.0001580193,0.00005175667,0.00005832423,0.0001247205,0.0001729788,0.0002457967,0.00001425381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013959,"about_ca_system_score_gemma":0.00003219284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004894012,"about_ca_topic_score_gemma":0.00002214083,"domain_scores_codex":[0.9992438,0.00002053683,0.0001127659,0.0003687433,0.00003884605,0.0002153183],"domain_scores_gemma":[0.9989988,0.00005246487,0.0001130554,0.0006621244,0.0001014815,0.00007205075],"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":[0.00007888932,0.00004834633,0.0007483962,0.0002526797,0.0002955804,0.0001011948,0.000130133,0.9789661,0.003216269,0.004918904,0.00779205,0.003451475],"study_design_scores_gemma":[0.0004443016,0.00002336863,0.0006128646,0.0002015018,0.0001320002,0.000001393689,0.00004022604,0.9708607,0.01159802,0.0146308,0.001023334,0.0004314926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.635314,0.00007379363,0.3586489,0.00001661142,0.0007162876,0.0005619657,0.00007618676,0.001064403,0.003527838],"genre_scores_gemma":[0.9982049,0.0001248665,0.001104607,0.00001979567,0.000109473,0.00000259152,0.0001152515,0.00004385408,0.0002746642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3628908,"threshold_uncertainty_score":0.9999361,"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."}}