{"id":"W1991157971","doi":"10.1145/968363.968378","title":"An eye for an eye","year":2004,"lang":"en","type":"article","venue":"","topic":"Intraocular Surgery and Lenses","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Optometry; Artificial intelligence; 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":[0.0001234885,0.00006244186,0.0001140129,0.00003265858,0.00004910139,0.00001003397,0.00003241759,0.00005206235,0.0003182368],"category_scores_gemma":[0.00002915667,0.0000469201,0.00005644797,0.00004023316,0.00002116913,0.00010089,0.000002658889,0.00004776787,0.00005103768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001470338,"about_ca_system_score_gemma":0.00006946181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006386794,"about_ca_topic_score_gemma":0.00002928916,"domain_scores_codex":[0.9995451,0.000009665052,0.00009006273,0.000128784,0.00007353104,0.0001528866],"domain_scores_gemma":[0.9996135,0.00001165037,0.00001213692,0.000204194,0.00004940657,0.0001091228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002536967,0.005167223,0.06560942,0.0004956122,0.0003715159,0.0004715828,0.004886291,0.0003090377,0.5583594,0.2475027,0.01004449,0.1042457],"study_design_scores_gemma":[0.006614141,0.00597224,0.08819857,0.000141207,0.0002460687,0.0000683524,0.001547259,0.0006787903,0.5718288,0.006992574,0.3170104,0.0007015716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819111,0.00003385481,0.004959463,0.001235922,0.000139601,0.0002065699,0.000002649077,0.000103186,0.01140764],"genre_scores_gemma":[0.9892333,0.000003609732,0.005404898,0.002512868,0.0003006031,0.00001137966,0.00003860737,0.00001249213,0.002482265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3069659,"threshold_uncertainty_score":0.348447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02426432167914694,"score_gpt":0.331859101042373,"score_spread":0.3075947793632261,"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."}}