{"id":"W4398913998","doi":"10.7910/dvn/zbrtjh","title":"Replication Data for: Beyond the Unit Root Question: Uncertainty and Inference","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; Mathematics; Statistics; Artificial intelligence; Linguistics; Philosophy","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":[0.01274685,0.001261331,0.001546076,0.003730666,0.0017762,0.003288722,0.005046383,0.003194215,0.02360558],"category_scores_gemma":[0.08164552,0.0005628217,0.001475161,0.007430096,0.001426099,0.002401314,0.00358283,0.003348972,0.02234866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002310081,"about_ca_system_score_gemma":0.003532162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01876397,"about_ca_topic_score_gemma":0.0375427,"domain_scores_codex":[0.9913856,0.0033806,0.001137057,0.001697774,0.00195795,0.0004410757],"domain_scores_gemma":[0.9654052,0.01261517,0.002830876,0.01373425,0.00473366,0.000680806],"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":[0.0002620841,0.00008356387,0.01150083,0.0008438615,0.0002287934,0.0001305397,0.0001355512,0.001657877,0.0002011813,0.01055108,0.9596614,0.01474329],"study_design_scores_gemma":[0.000848718,0.00005579677,0.01631639,0.000580569,0.0001258603,0.0004024116,0.0003875696,0.005494967,0.001102401,0.03341493,0.9411626,0.0001078381],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008093329,0.00168003,0.007575773,0.004768414,0.0005697669,0.0002203413,0.9688436,0.001735888,0.006512844],"genre_scores_gemma":[0.02630197,0.0002774648,0.0120556,0.001137486,0.00012774,0.0008779051,0.9552282,0.0003640355,0.003629531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02360558,"threshold_uncertainty_score":0.07896852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07338997824271198,"score_gpt":0.3310777180133337,"score_spread":0.2576877397706217,"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."}}