{"id":"W2398001269","doi":"10.2352/cic.2011.19.1.art00030","title":"Fast Colour Vesselness","year":2011,"lang":"en","type":"article","venue":"Color and Imaging Conference","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer vision; Grayscale; Computer science; Image (mathematics); Pixel; Hessian matrix; Pattern recognition (psychology); Mathematics","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":[0.0008079196,0.0008974334,0.0006954721,0.002425805,0.0003629665,0.001695845,0.0008481793,0.00076085,0.009321371],"category_scores_gemma":[0.003215654,0.0003813973,0.000510709,0.001511025,0.0004406729,0.001845606,0.001293874,0.0008556531,0.00491165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503387,"about_ca_system_score_gemma":0.0004873805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001674815,"about_ca_topic_score_gemma":0.001862857,"domain_scores_codex":[0.9994629,0.00005649404,0.0000149502,0.0001045859,0.0002964992,0.00006453643],"domain_scores_gemma":[0.9986602,0.0003032584,0.0001290131,0.0002687038,0.000575867,0.0000629051],"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":[0.0003816946,0.00005900166,0.002213092,0.0003038989,0.00007660457,0.000160173,0.0001641666,0.01450464,0.1827857,0.01133242,0.0151954,0.7728231],"study_design_scores_gemma":[0.00008616503,0.0003742613,0.01367719,0.00009685272,0.0001312614,0.002058709,0.0001775028,0.5605204,0.3462093,0.02533121,0.05115597,0.0001811251],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01877239,0.0004756357,0.9704825,0.0001133614,0.0001239757,0.00009767916,0.0005129334,0.005080158,0.004341302],"genre_scores_gemma":[0.1788299,0.0008450053,0.8085972,0.000126366,0.0001179608,0.0001097848,0.001175498,0.00103201,0.009166198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009321371,"threshold_uncertainty_score":0.03118306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03855580938296822,"score_gpt":0.2652537808638498,"score_spread":0.2266979714808816,"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."}}