{"id":"W6958200935","doi":"10.6084/m9.figshare.19785625.v1","title":"Additional file 1 of Prioritising attributes for tuberculosis preventive treatment regimens: a modelling analysis","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Table (database); Key (lock); Tuberculosis; Data collection","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":[],"category_scores_codex":[0.0001234306,0.0001371393,0.0002947008,0.0001453311,0.0004160086,0.00006464715,0.0002999679,0.00002838382,0.9960942],"category_scores_gemma":[0.001194418,0.0001362223,0.0002184937,0.0005012411,0.00001502092,0.0001450802,0.0001833854,0.00005136338,0.0002412787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170587,"about_ca_system_score_gemma":0.0001233013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003411192,"about_ca_topic_score_gemma":0.000005779796,"domain_scores_codex":[0.9985224,0.0001521154,0.0002836272,0.0004117962,0.0003671477,0.0002629144],"domain_scores_gemma":[0.9974208,0.001814724,0.0003209352,0.0002516086,0.0001419142,0.00005005649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001562462,0.0000614439,0.000001988248,0.00004451297,0.00004982493,0.000002094371,0.0001195724,0.1414142,0.001327913,0.000004978559,0.8568448,0.0001129365],"study_design_scores_gemma":[0.0002460682,0.000371097,0.0002192728,0.0004186722,0.0001354681,0.00000858186,0.0001138815,0.2249008,0.007078835,0.0001992346,0.766009,0.0002991487],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001043255,0.00004091611,0.0001975608,0.00002914344,0.00003471106,0.0003370972,0.9980874,0.00005664862,0.0001732666],"genre_scores_gemma":[0.005561443,3.196752e-7,0.02747081,0.00002694913,0.00006639245,0.005315375,0.9599727,0.00001620255,0.001569844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9958529,"threshold_uncertainty_score":0.5554986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03555324493134038,"score_gpt":0.2670994611072444,"score_spread":0.231546216175904,"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."}}