{"id":"W6913007055","doi":"10.5683/sp3/bbzfoy","title":"Replication Data and Code for: Reworking Wild Bootstrap Based Inference for Clustered Errors","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Replicate; Replication (statistics); Inference; Monte Carlo method; Set (abstract data type); Data set; Code (set theory)","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":[],"consensus_categories":[],"category_scores_codex":[0.01035604,0.00302617,0.002729203,0.003683414,0.00221664,0.00495063,0.005647765,0.002366791,0.2851537],"category_scores_gemma":[0.04903862,0.002493255,0.003350078,0.005021991,0.001365953,0.003138729,0.002872412,0.004503253,0.2144747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001898605,"about_ca_system_score_gemma":0.004171607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01844933,"about_ca_topic_score_gemma":0.03216743,"domain_scores_codex":[0.9940447,0.001597362,0.0006700615,0.002084575,0.001223151,0.0003801241],"domain_scores_gemma":[0.9683734,0.01368875,0.001086353,0.01211046,0.004009499,0.0007314723],"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":[0.0001291225,0.00002983976,0.001575582,0.000587457,0.0001229159,0.00003675475,0.00008995279,0.0008143249,0.0004038171,0.001403222,0.9891469,0.005660004],"study_design_scores_gemma":[0.001067234,0.00004879426,0.006711252,0.0004060103,0.0002294991,0.0002196027,0.000116757,0.003049625,0.002437691,0.01649116,0.9690415,0.0001808247],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005103121,0.00008035643,0.009001158,0.000195186,0.0002270693,0.0001414784,0.9687763,0.01798966,0.003078478],"genre_scores_gemma":[0.004715588,0.00008245051,0.02548728,0.0006319458,0.00009021525,0.001352627,0.9319412,0.03059308,0.00510553],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2851537,"threshold_uncertainty_score":0.9539342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1565126202678611,"score_gpt":0.3876563390397862,"score_spread":0.2311437187719251,"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."}}