{"id":"W4244631597","doi":"10.17116/profmed201720216-18","title":"CINDI-Canada","year":2017,"lang":"en","type":"article","venue":"Russian Journal of Preventive Medicine","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001659558,0.0002040533,0.0005992777,0.0001884658,0.0003602161,0.00003646719,0.001030897,0.00004258254,0.001836301],"category_scores_gemma":[0.001303623,0.0001365205,0.0001242134,0.00008821027,0.0003981057,0.0004024544,0.00008225538,0.0004608448,0.0000972479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002946215,"about_ca_system_score_gemma":0.0008072095,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01776096,"about_ca_topic_score_gemma":0.04528712,"domain_scores_codex":[0.997597,0.0001809008,0.0007366729,0.0001540055,0.001000832,0.0003305727],"domain_scores_gemma":[0.996311,0.00008359173,0.00239043,0.000589075,0.0002112581,0.0004146292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001232053,0.0004991259,0.1784594,0.000191753,0.002479092,0.009872514,0.00342743,0.00002045653,0.02229654,0.01793589,0.7039501,0.0596357],"study_design_scores_gemma":[0.0059156,0.000751567,0.8907041,0.001888517,0.0003884981,0.0009256443,0.0006763875,0.00001815002,0.002398928,0.005127616,0.09092637,0.0002786546],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6600961,0.00498896,0.002854397,0.04962378,0.008461338,0.0006545742,0.00007500489,0.00003819901,0.2732076],"genre_scores_gemma":[0.9946565,0.0000317484,0.0004009668,0.0001371498,0.001574256,9.646536e-7,0.000001696054,0.00004390497,0.003152792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7122447,"threshold_uncertainty_score":0.9990761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016050005258981,"score_gpt":0.3030356980289067,"score_spread":0.2828751979763169,"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."}}