{"id":"W4398952834","doi":"10.7910/dvn/zbrtjh/ylvh8c","title":"Table2Sim1kLoadModules.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); Unit root; Computer science; Econometrics; Mathematics; Statistics; 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":[0.002795066,0.003300132,0.002583771,0.004340546,0.001480464,0.00485911,0.005496269,0.002888046,0.3554972],"category_scores_gemma":[0.02618949,0.001337973,0.002642245,0.005887222,0.0008640041,0.00225824,0.00289182,0.002684854,0.2229277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001699373,"about_ca_system_score_gemma":0.003713899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01010166,"about_ca_topic_score_gemma":0.02095396,"domain_scores_codex":[0.9979761,0.0004769898,0.0002080726,0.0007792049,0.0003320365,0.0002275596],"domain_scores_gemma":[0.9904408,0.005687926,0.0005183902,0.00189889,0.0009285294,0.0005254254],"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":[0.00006390706,0.00001828499,0.0005234702,0.001074876,0.00006788211,0.00001883352,0.00001915662,0.0002828521,0.0000613314,0.0007642428,0.9953354,0.001769718],"study_design_scores_gemma":[0.0008907935,0.00003444163,0.00175053,0.0007094944,0.000133837,0.0001072853,0.0000579581,0.001052235,0.0005235,0.00883962,0.9858424,0.00005794683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.00009577513,0.0001077611,0.0003151173,0.00009564132,0.00003990355,0.00002115277,0.996609,0.001724443,0.0009913038],"genre_scores_gemma":[0.001317214,0.000183579,0.001846165,0.000261751,0.00003176633,0.000445161,0.9925594,0.001650784,0.001704087],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.6445029,"threshold_uncertainty_score":0.9193051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864174708522784,"score_gpt":0.253912330929257,"score_spread":0.2252705838440291,"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."}}