{"id":"W4301934633","doi":"","title":"Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction","year":2017,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thresholding; Segmentation; Artificial intelligence; Extraction (chemistry); Computer vision; Computer science; Fuzzy logic; Pattern recognition (psychology); Image (mathematics); Chromatography; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007918891,0.0005696071,0.0005158052,0.002517849,0.0004110305,0.001073142,0.0006956155,0.000903551,0.0007153265],"category_scores_gemma":[0.001475753,0.0003471402,0.0007691112,0.001212617,0.0003969358,0.0006130654,0.0004340726,0.0003940446,0.0003332276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309441,"about_ca_system_score_gemma":0.0006828083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006151368,"about_ca_topic_score_gemma":0.00496003,"domain_scores_codex":[0.999579,0.00004795592,0.0000329778,0.00008201901,0.0001949549,0.00006300514],"domain_scores_gemma":[0.9994475,0.0001372022,0.00006715131,0.00005240674,0.0002730088,0.00002275124],"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":[0.0005660888,0.0001449026,0.00846181,0.0004047076,0.0001473648,0.0006642663,0.0004515983,0.0373878,0.2903517,0.002124444,0.002364911,0.6569304],"study_design_scores_gemma":[0.00002586711,0.0001326513,0.0146816,0.000054181,0.0001083166,0.0009000656,0.0001859988,0.8450974,0.1344092,0.001247736,0.003088946,0.00006812614],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1968777,0.001045198,0.7983835,0.0001992411,0.00007430504,0.0001353126,0.0001493863,0.0009484822,0.00218677],"genre_scores_gemma":[0.5117227,0.000610048,0.4854096,0.00006447131,0.00004850542,0.00006310602,0.0002203099,0.00009710307,0.001764148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006151368,"threshold_uncertainty_score":0.01223117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2564433666312963,"score_gpt":0.5681793438464245,"score_spread":0.3117359772151282,"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."}}