{"id":"W4386307409","doi":"10.18280/ts.400410","title":"Enhancing Cataract Detection Precision: A Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","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":[0.0005857033,0.000125971,0.0002171288,0.0002337901,0.0001753808,0.0000420024,0.0000586405,0.00004166628,0.0002446179],"category_scores_gemma":[0.00007068097,0.0001060772,0.0001480672,0.0005814658,0.00002489305,0.00008534651,0.00002528492,0.0002395184,0.0002293748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005284609,"about_ca_system_score_gemma":0.00001928865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004136886,"about_ca_topic_score_gemma":0.000005403529,"domain_scores_codex":[0.9987587,0.00006503613,0.0002571096,0.0002801585,0.0003819468,0.0002571004],"domain_scores_gemma":[0.9995742,0.00006488887,0.00007096045,0.0001189676,0.00006339308,0.0001076279],"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":[0.000356888,0.0003156173,0.01340397,0.0002889554,0.0003796248,0.0001591627,0.002943246,0.00767129,0.5337877,0.00002189346,0.0004386501,0.4402331],"study_design_scores_gemma":[0.003054212,0.001124295,0.09032315,0.0003848872,0.0009453431,0.0002724578,0.004422513,0.7626935,0.1201171,0.00009691386,0.01591543,0.000650191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9020644,0.0001189876,0.09410214,0.0002877571,0.00005888845,0.0001950231,7.391977e-7,0.0003477703,0.002824236],"genre_scores_gemma":[0.9969239,0.00003693718,0.0009406826,0.0000876738,0.0003005828,0.00002923095,0.00007018076,0.00001987352,0.001590989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7550222,"threshold_uncertainty_score":0.4325703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817346419132436,"score_gpt":0.2712722337869208,"score_spread":0.2530987695955965,"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."}}