{"id":"W4388023864","doi":"10.1037/met0000596.supp","title":"Supplemental Material for Equivalence Testing for Linear Regression","year":2023,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Equivalence (formal languages); Statistics; Regression analysis; Linear regression; Mathematics; Econometrics; Statistical hypothesis testing; Psychology; Regression; Discrete mathematics","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":[],"category_scores_codex":[0.003034964,0.001417099,0.0008208245,0.002245362,0.0006193231,0.001036103,0.002107833,0.001217192,0.6584584],"category_scores_gemma":[0.03801846,0.0007833615,0.0009327334,0.001663997,0.0002841869,0.00135056,0.001230144,0.001251353,0.1272845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006024718,"about_ca_system_score_gemma":0.0009967962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002345975,"about_ca_topic_score_gemma":0.004167299,"domain_scores_codex":[0.9984762,0.0005844175,0.0001531793,0.0002376018,0.0004555244,0.00009303412],"domain_scores_gemma":[0.9743001,0.02074144,0.0004016449,0.002001246,0.002367382,0.0001881531],"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":[0.000230516,0.0006804692,0.001748816,0.0008380185,0.0001040737,0.0001404244,0.0001097295,0.001784833,0.001477319,0.02917477,0.7948678,0.1688433],"study_design_scores_gemma":[0.001409136,0.0004601292,0.01842856,0.0007505735,0.0001792737,0.001401363,0.0002596895,0.04816954,0.008958114,0.2769997,0.642813,0.0001709205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.005009872,0.0005227693,0.6526238,0.001643935,0.001446878,0.00111913,0.2534618,0.0322701,0.0519018],"genre_scores_gemma":[0.06499884,0.0006725977,0.6017829,0.001694484,0.00106488,0.005952853,0.2474434,0.01786835,0.05852178],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.6584584,"threshold_uncertainty_score":0.4871677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1517067818175471,"score_gpt":0.4677150525645851,"score_spread":0.316008270747038,"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."}}