{"id":"W2981265883","doi":"10.1049/el.2019.2123","title":"RSF model with SCE‐based global constraint for image segmentation","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; University of Alberta","keywords":"Constraint (computer-aided design); Image segmentation; Segmentation; Image (mathematics); Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Algorithm; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009159156,0.0009316457,0.001104158,0.0008933988,0.0003124433,0.0008637078,0.001996384,0.001730876,0.001761227],"category_scores_gemma":[0.001882131,0.0005134686,0.00121705,0.001060371,0.0007491498,0.001715376,0.0008081906,0.001073061,0.0006325404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007337595,"about_ca_system_score_gemma":0.0008327338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004067668,"about_ca_topic_score_gemma":0.003136758,"domain_scores_codex":[0.9995551,0.00008362871,0.00002378071,0.0001140626,0.0001903381,0.0000331346],"domain_scores_gemma":[0.9995368,0.0001953593,0.00006194083,0.00006126872,0.0001256316,0.00001913187],"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":[0.00005633299,0.00002629655,0.0003117206,0.00009281083,0.00005283977,0.0000959083,0.00007245423,0.9030726,0.01020598,0.01287951,0.0012655,0.07186803],"study_design_scores_gemma":[0.000001747747,0.00000633098,0.00003523353,0.000002661603,0.000003502917,0.00001757249,0.00000133729,0.9980889,0.0005743587,0.0008997511,0.0003641876,0.000004329908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002792477,0.0002327743,0.9960448,0.00007172755,0.00001515166,0.00002073999,0.0000288491,0.0001833345,0.0006101963],"genre_scores_gemma":[0.4998346,0.001057087,0.4879304,0.000311328,0.0001267017,0.0005123858,0.0005651962,0.000484977,0.009177324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004067668,"threshold_uncertainty_score":0.008087993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007744714785534058,"score_gpt":0.263221854690372,"score_spread":0.2554771399048379,"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."}}