{"id":"W4250250380","doi":"10.1111/ceo.13625","title":"Scientific Program","year":2019,"lang":"en","type":"article","venue":"Clinical and Experimental Ophthalmology","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Ophthalmology; Medical physics; Family medicine; Optometry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005617964,0.0001062337,0.0003869043,0.00006972591,0.0001193247,0.0001013284,0.0001559815,0.00009348051,0.01339605],"category_scores_gemma":[0.00003404805,0.0001075185,0.0001131604,0.00008281246,0.0006884808,0.0001360688,0.0002168171,0.00009393113,0.03040824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001422684,"about_ca_system_score_gemma":0.000007080695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000192987,"about_ca_topic_score_gemma":1.256476e-7,"domain_scores_codex":[0.9985471,0.00001380834,0.0005105091,0.0006534962,0.00001796601,0.0002571135],"domain_scores_gemma":[0.9994649,0.00004713351,0.0001246928,0.000247762,0.0000111428,0.0001044229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008177978,0.001059172,0.8351462,0.00001798837,0.0001158927,0.00003243375,0.0005160947,0.000002730234,0.0001092559,0.1265061,0.03530877,0.001103558],"study_design_scores_gemma":[0.003327299,0.001580529,0.085326,0.00001459842,0.000007561269,0.0002013588,0.001844579,0.0008929883,0.0004761502,0.01593279,0.8896521,0.0007440229],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7314288,0.001380835,4.742529e-7,0.00007374148,0.004703002,0.0001787377,0.00002623675,0.00002503113,0.2621832],"genre_scores_gemma":[0.88143,0.00001147969,0.0003363764,0.00009128427,0.00006404987,0.00002277405,0.000006804973,0.000007274589,0.1180299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8543434,"threshold_uncertainty_score":0.9875059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061280527491712,"score_gpt":0.3467288858730395,"score_spread":0.2406008331238683,"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."}}