{"id":"W4389879453","doi":"10.1167/jov.23.15.35","title":"Poster Session I: Leveraging AI to accelerate scientific discoveries","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Post hoc; Retinal; Session (web analytics); Artificial intelligence; Machine learning; Medicine; Ophthalmology","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":[0.0006236274,0.00007555763,0.0002190794,0.0005189884,0.0001446208,0.0002314056,0.00008270059,0.0000218457,0.00007805697],"category_scores_gemma":[0.0001309731,0.00004786337,0.0001453502,0.0007460245,0.0000303538,0.0003347114,0.00006383719,0.0001785645,0.0001316919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000371765,"about_ca_system_score_gemma":0.00006662473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003813076,"about_ca_topic_score_gemma":3.727558e-7,"domain_scores_codex":[0.998921,0.00003186633,0.0002958731,0.0001236655,0.0004742056,0.0001534035],"domain_scores_gemma":[0.9992893,0.00002849098,0.0001241577,0.0001378898,0.0002702448,0.0001499635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000406843,0.0001042175,0.03753702,0.00007667277,0.00006764612,0.0005361217,0.001549736,0.0003252265,0.8019874,0.000005816951,0.1238511,0.03355223],"study_design_scores_gemma":[0.002403691,0.001661422,0.7804131,0.005434092,0.000437898,0.0008370162,0.001951776,0.005144163,0.08276767,0.0005010144,0.1180578,0.0003903931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9761111,0.00007363239,0.0004365655,0.02261049,0.0004415719,0.00003758087,9.022834e-7,0.00001802132,0.0002701941],"genre_scores_gemma":[0.9937571,0.00002951917,0.0003048233,0.0008178605,0.000203177,2.762225e-7,0.000004264715,0.00001072868,0.004872211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7428761,"threshold_uncertainty_score":0.223145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03261042728475871,"score_gpt":0.3700053023579756,"score_spread":0.3373948750732169,"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."}}