{"id":"W4255232221","doi":"10.18642/jsata_7100121682","title":"META ANALYSES OF CORRELATED MULTIPLE BASELINE TIME SERIES DESIGN INTERVENTION MODELS USING JR ESTIMATE","year":2016,"lang":"en","type":"article","venue":"Journal of Statistics Advances in Theory and Applications","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Baseline (sea); Series (stratigraphy); Statistics; Time series; Meta-analysis; Computer science; Mathematics; Medicine; Geology; Internal medicine","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":[0.1249731,0.002009436,0.007981918,0.004122539,0.0005643761,0.002867156,0.002582284,0.002562125,0.004248595],"category_scores_gemma":[0.2509473,0.00107029,0.02407623,0.003725953,0.001075882,0.002525753,0.001810811,0.003068935,0.0004268051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141044,"about_ca_system_score_gemma":0.001930534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007309114,"about_ca_topic_score_gemma":0.0008858914,"domain_scores_codex":[0.7516779,0.2265552,0.008317103,0.00770325,0.005189751,0.0005567017],"domain_scores_gemma":[0.7256935,0.2381418,0.01186533,0.02079686,0.003174549,0.0003279091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.01010737,0.0005067309,0.01669034,0.07625552,0.646138,0.0004614924,0.0006107406,0.02128622,0.002476556,0.03243132,0.003661008,0.1893747],"study_design_scores_gemma":[0.007933324,0.009169854,0.0167725,0.01029117,0.7952691,0.0006733753,0.0003855412,0.06516083,0.005588091,0.06441229,0.02398515,0.0003587448],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03355534,0.1098687,0.8445541,0.001277445,0.001367273,0.004794789,0.001546891,0.001003358,0.002032019],"genre_scores_gemma":[0.4621503,0.01985016,0.4921908,0.00134428,0.0006847929,0.02019993,0.001397069,0.0002950581,0.001887619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1249731,"threshold_uncertainty_score":0.6609288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673201094768954,"score_gpt":0.3201153507063154,"score_spread":0.2733833397586258,"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."}}