{"id":"W2310919266","doi":"10.5539/cis.v9n2p23","title":"High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Computer vision; Process (computing); Image processing; Segmentation; Noise (video); Filter (signal processing); Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005923253,0.0004631834,0.0003814993,0.001302816,0.00033736,0.001008166,0.0009297198,0.0007959733,0.00377216],"category_scores_gemma":[0.001710553,0.0004096444,0.0005691843,0.001161632,0.000378905,0.0009044252,0.0006200963,0.000562166,0.001572459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003645319,"about_ca_system_score_gemma":0.0008276654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001152276,"about_ca_topic_score_gemma":0.002152428,"domain_scores_codex":[0.9993843,0.00009080052,0.00003579121,0.00009529189,0.000361455,0.00003238583],"domain_scores_gemma":[0.9993056,0.0001990966,0.0001072082,0.0001497177,0.0002169893,0.00002133648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001977576,0.00007139379,0.001561519,0.0004230154,0.00006743622,0.0003603236,0.0002113193,0.01283448,0.3077038,0.007056553,0.003420954,0.6660914],"study_design_scores_gemma":[0.00006203833,0.0004373557,0.01087718,0.0001042445,0.0001288841,0.004094766,0.0002003648,0.5129116,0.4118675,0.008391537,0.05080143,0.0001230907],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009600198,0.0003245853,0.9877511,0.0001030626,0.00003624423,0.00006099371,0.00004985416,0.0009675433,0.001106344],"genre_scores_gemma":[0.07135664,0.0003684627,0.9261884,0.00007801809,0.00003342747,0.00007762578,0.0001713997,0.0001653716,0.001560659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00377216,"threshold_uncertainty_score":0.01261914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01136640975215944,"score_gpt":0.3261072894291305,"score_spread":0.3147408796769711,"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."}}