{"id":"W2546164798","doi":"10.1097/iae.0000000000001354","title":"Iris Atrophy","year":2016,"lang":"en","type":"article","venue":"Retina","topic":"Ocular and Laser Science Research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Humanities; IRIS (biosensor); Art; Philosophy; Gerontology; Medicine; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004209735,0.000901296,0.0008315428,0.001796253,0.001569315,0.001076944,0.0004213547,0.001060758,0.05020394],"category_scores_gemma":[0.001902718,0.000231122,0.0007803155,0.001009787,0.0005587765,0.00111456,0.0007430596,0.001472192,0.01086394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005597,"about_ca_system_score_gemma":0.0008205065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004574659,"about_ca_topic_score_gemma":0.004974173,"domain_scores_codex":[0.9994449,0.00005827663,0.00004659922,0.0001682965,0.0001950524,0.00008691777],"domain_scores_gemma":[0.9993309,0.0001219616,0.0001540003,0.0001104123,0.0001610399,0.0001216883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.00134329,0.0005150512,0.06338967,0.001100277,0.000398273,0.1450256,0.0006656402,0.0004890103,0.03599691,0.01942547,0.1387295,0.5929213],"study_design_scores_gemma":[0.0002228405,0.0003937962,0.1289386,0.001492251,0.000301107,0.6094105,0.0005474605,0.001723406,0.0109823,0.008605316,0.2372603,0.0001222085],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2355751,0.07810441,0.01951596,0.02510433,0.004517303,0.0007328458,0.006319517,0.004461872,0.6256687],"genre_scores_gemma":[0.7480192,0.02055229,0.008202539,0.008685361,0.001751421,0.0002023542,0.003301921,0.0004911975,0.2087938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05020394,"threshold_uncertainty_score":0.1679489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813778204188163,"score_gpt":0.3091724202462525,"score_spread":0.2910346382043709,"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."}}