{"id":"W4399012760","doi":"10.7910/dvn/zbrtjh/t9tlsn","title":"Figure2LoadModules.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Replication (statistics); Inference; Root (linguistics); Computer science; Statistics; Mathematics; Artificial intelligence; Philosophy; Linguistics","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":["insufficient_payload"],"category_scores_codex":[0.002300958,0.004421679,0.002214779,0.003205424,0.001159234,0.003677497,0.005012134,0.002214664,0.3153052],"category_scores_gemma":[0.01286383,0.001620436,0.002379302,0.003872863,0.0007786553,0.002162322,0.002870338,0.00245264,0.2779488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001370366,"about_ca_system_score_gemma":0.00234861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01042366,"about_ca_topic_score_gemma":0.02090842,"domain_scores_codex":[0.9985003,0.0003173699,0.0001183291,0.000610561,0.0002791327,0.0001742834],"domain_scores_gemma":[0.9952969,0.002282677,0.0002119399,0.001309293,0.0005754878,0.0003237874],"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":[0.00007911215,0.00001668333,0.0003862417,0.0005657129,0.00005082169,0.00001412836,0.00002313809,0.0002721518,0.0001275519,0.0007048274,0.9956074,0.002152296],"study_design_scores_gemma":[0.0007002124,0.00002867178,0.001388821,0.0002857669,0.0000801851,0.00006624738,0.00004017578,0.001152314,0.001059516,0.0071692,0.9879798,0.00004917667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.0001682915,0.0001102473,0.0009956886,0.0001282101,0.00004865732,0.00003209538,0.9857757,0.01050027,0.002240894],"genre_scores_gemma":[0.001655702,0.0001393982,0.002726551,0.0002865521,0.00002885706,0.0004101785,0.9872755,0.005401206,0.002075977],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.6846948,"threshold_uncertainty_score":0.9766341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961951228597605,"score_gpt":0.2564553709340389,"score_spread":0.2268358586480628,"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."}}