{"id":"W4317039737","doi":"10.18280/ria.360608","title":"Severity Classification of Diabetic Retinopathy Using Ensemble Stacking Method","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diabetic retinopathy; Stacking; Medicine; Ensemble learning; Retinopathy; Pattern recognition (psychology); Ophthalmology; Artificial intelligence; Computer science; Diabetes mellitus; Physics; Endocrinology; Nuclear magnetic resonance","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009877428,0.000115334,0.0003280318,0.0001821528,0.0002280367,0.00001546235,0.0001437406,0.00003130325,0.0005763821],"category_scores_gemma":[0.0001826966,0.0001206328,0.0001734403,0.0007810905,0.00006613088,0.00005311806,0.0000960036,0.0002732158,0.00002127331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202912,"about_ca_system_score_gemma":0.00007087635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000127071,"about_ca_topic_score_gemma":0.000001341327,"domain_scores_codex":[0.9984553,0.0002023105,0.0004892682,0.0003303465,0.0002947527,0.0002279487],"domain_scores_gemma":[0.9989406,0.0001333401,0.0002372281,0.0004573253,0.0001559097,0.00007553142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001056474,0.0004170223,0.02175258,0.0002414701,0.00007021627,0.00003944779,0.001819494,0.04028764,0.8551786,0.0007212111,0.0001382096,0.07922844],"study_design_scores_gemma":[0.0000435847,0.0001528163,0.0008394786,0.00008778537,0.0001556729,0.0001012918,0.002605882,0.7770481,0.2174712,0.0004231058,0.000946917,0.0001242538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7581202,0.0002812415,0.2385254,0.0005229078,0.0001267283,0.0001769693,0.000008549214,0.0000418688,0.002196146],"genre_scores_gemma":[0.9851766,0.00001642202,0.01353585,0.00008537236,0.00004649218,0.00001208346,0.00001894839,0.0000192997,0.001088884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7367604,"threshold_uncertainty_score":0.631098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0807365816351201,"score_gpt":0.3517476744779537,"score_spread":0.2710110928428336,"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."}}