{"id":"W2909591281","doi":"10.1007/978-3-319-89635-9_6","title":"Ties, Time Series, and Regression","year":2018,"lang":"en","type":"book-chapter","venue":"Use R!","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Copula (linguistics); Series (stratigraphy); Covariate; Regression; Econometrics; Regression analysis; Time series; Computer science; Cross-sectional regression; Statistics; Mathematics; Polynomial regression; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001919869,0.0002308084,0.0004662673,0.0001456308,0.0001078406,0.00008101413,0.0001050495,0.0003962372,0.001959693],"category_scores_gemma":[0.00006529156,0.0002508231,0.00008133929,0.00001741356,0.00009577951,0.000242481,0.0001101291,0.0002185723,0.002233086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000552454,"about_ca_system_score_gemma":0.00002146523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000896553,"about_ca_topic_score_gemma":0.0000254788,"domain_scores_codex":[0.9989219,0.000002616736,0.0004245837,0.0004431781,0.00003116931,0.0001766018],"domain_scores_gemma":[0.9992216,0.00002582003,0.000272871,0.000373762,0.00003486704,0.00007104919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009141025,0.00001565542,0.00118257,0.00009433427,0.00006502146,0.00001317103,0.0005729542,0.000002140961,0.000003085509,0.9374818,0.05644041,0.004037511],"study_design_scores_gemma":[0.0000939836,0.00005864086,0.0001696807,0.0001166796,0.000006284975,0.000003066515,0.000001160258,0.0008890754,0.000002568952,0.1847971,0.8136078,0.0002539482],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01036294,0.01342211,0.000545308,0.000232307,0.0008528199,0.0003577246,0.001032366,0.0001333879,0.973061],"genre_scores_gemma":[0.00163747,0.003296396,0.001263397,0.00009845974,0.0003994404,0.000002890568,0.00008719575,0.00006685737,0.9931479],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.7571674,"threshold_uncertainty_score":0.9999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04765437101246926,"score_gpt":0.205213427985779,"score_spread":0.1575590569733098,"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."}}