{"id":"W4254433509","doi":"10.1109/cbms.2004.1311771","title":"RetsoftPlus: a tool for retinal image analysis","year":2004,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Retinal; Image (mathematics); Computer vision; Artificial intelligence; Ophthalmology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002473633,0.0001124365,0.000356298,0.0002548246,0.00006847012,0.00003767799,0.00006251669,0.00003871475,0.0003243729],"category_scores_gemma":[0.000225129,0.00008417948,0.0005572645,0.0007955843,0.00005433936,0.0000657274,0.00001511012,0.00008447213,0.00006636894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005785253,"about_ca_system_score_gemma":0.00005742038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121802,"about_ca_topic_score_gemma":0.000009729527,"domain_scores_codex":[0.9990681,0.00001000321,0.0002341457,0.0002713027,0.0001957516,0.0002207389],"domain_scores_gemma":[0.9993261,0.00003917675,0.00005218119,0.0003046752,0.0001827432,0.00009512762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005102118,0.00383986,0.452033,0.001799584,0.03526866,0.001487801,0.00392379,0.002027014,0.3415836,0.03208393,0.06339148,0.05745915],"study_design_scores_gemma":[0.043943,0.00558164,0.351884,0.00107223,0.1561052,0.001099571,0.005730469,0.06969111,0.2398665,0.02791184,0.09242372,0.004690646],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5578003,0.0001177192,0.4080768,0.01523416,0.00003279043,0.0003132377,0.00001152913,0.0002450561,0.01816845],"genre_scores_gemma":[0.8900317,0.0000162474,0.09973258,0.001037799,0.0001233432,0.00001245561,0.00006052493,0.0000149428,0.008970403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322314,"threshold_uncertainty_score":0.3551656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073832788215695,"score_gpt":0.306939117045048,"score_spread":0.2962007891628911,"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."}}