{"id":"W6939531629","doi":"10.6084/m9.figshare.14570720.v1","title":"Additional file 1 of Using random forests to model 90-day hometime in people with stroke","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto; University of Calgary","funders":"","keywords":"Table (database); Demographics; Random forest; Cohort; Stroke (engine); Pairwise comparison","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003773571,0.00009095017,0.00024606,0.00005537271,0.00002340066,0.00001816946,0.00008142139,0.00004672675,0.9710664],"category_scores_gemma":[0.01472192,0.00007787841,0.00003838832,0.0002178436,0.000004853908,0.0000465372,0.00006172939,0.00008666112,0.000114373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002444184,"about_ca_system_score_gemma":0.0002001935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002654246,"about_ca_topic_score_gemma":0.0002081503,"domain_scores_codex":[0.999247,0.0000587752,0.0001798499,0.0001613702,0.0001982543,0.0001547972],"domain_scores_gemma":[0.9947712,0.004770991,0.0000686248,0.0001554362,0.0001663665,0.00006732816],"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":[0.00002206761,0.00004976163,0.00001109151,0.0001303927,0.000008944984,0.00001836588,0.0000780609,0.0001815543,0.00001870081,0.0005370377,0.9978769,0.001067062],"study_design_scores_gemma":[0.003341848,0.0002246421,0.01189547,0.02466502,0.00006084269,0.00006815463,0.0002158363,0.749988,0.001848062,0.1739316,0.03264462,0.001115961],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.00007526021,0.000003629251,0.05442276,0.00001011798,0.000003712689,0.0001190396,0.943966,0.00001080771,0.001388664],"genre_scores_gemma":[0.0006763439,5.48003e-8,0.7441385,0.00003585795,0.00002457121,0.0002750729,0.2544475,0.00001474182,0.0003873763],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.970952,"threshold_uncertainty_score":0.9935775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08026284222746047,"score_gpt":0.3356042371225031,"score_spread":0.2553413948950427,"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."}}