{"id":"W6939665614","doi":"10.6084/m9.figshare.19785637.v1","title":"Additional file 4 of Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Calibration; Table (database); Tuberculosis; Key (lock); Markov chain Monte Carlo","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002566051,0.001127316,0.0009061749,0.001502445,0.0004993201,0.001366555,0.001950145,0.001233337,0.8444487],"category_scores_gemma":[0.02394177,0.0005862315,0.001448908,0.00234026,0.0002056698,0.001371618,0.0007738453,0.001103257,0.1438171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00145488,"about_ca_system_score_gemma":0.001533237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01988064,"about_ca_topic_score_gemma":0.02438513,"domain_scores_codex":[0.9992175,0.0003082188,0.00007812928,0.0001762646,0.0001277016,0.00009218566],"domain_scores_gemma":[0.9778801,0.01941596,0.0005936135,0.0006748799,0.001255114,0.0001804085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003210609,0.0001063664,0.005140222,0.002078573,0.0001359446,0.00008275549,0.00007676227,0.01307387,0.00008623982,0.002432515,0.9641572,0.0123086],"study_design_scores_gemma":[0.0049627,0.0003318577,0.02617549,0.002992863,0.0004801331,0.0004064382,0.0005648023,0.06261074,0.00102343,0.03296821,0.8672869,0.000196384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003618793,0.00002197837,0.0009734728,0.0001383811,0.00001730961,0.00007032361,0.9964194,0.0003812861,0.001616038],"genre_scores_gemma":[0.03458813,0.0002135748,0.0137156,0.0007002552,0.000111402,0.001789148,0.9295828,0.002208182,0.01709092],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8444487,"threshold_uncertainty_score":0.221875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07822025392728128,"score_gpt":0.3273526351068184,"score_spread":0.2491323811795372,"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."}}