{"id":"W4402847298","doi":"10.1080/07350015.2024.2407634","title":"Endogenous Kink Threshold Regression","year":2024,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Regression; Regression analysis; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000434993,0.00008701618,0.0001921402,0.0001344864,0.00006308325,0.0004171038,0.0002181206,0.0000330669,0.00002463598],"category_scores_gemma":[0.00003681313,0.00006383768,0.00003097748,0.00008347524,0.00003153429,0.0004376744,0.00006694346,0.0001194966,0.00001433976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004046929,"about_ca_system_score_gemma":0.0001558439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001402781,"about_ca_topic_score_gemma":0.000001316758,"domain_scores_codex":[0.9993458,0.00002388379,0.0003095757,0.0001204439,0.00007924115,0.0001210925],"domain_scores_gemma":[0.9994196,0.0001854112,0.0001251745,0.0001183533,0.0000876913,0.0000638216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005419343,0.00003694938,0.0005040144,0.0001901183,0.0001006758,0.002436784,0.0008725144,0.001217331,0.00374792,0.1476085,0.02302803,0.8202029],"study_design_scores_gemma":[0.00308115,0.0006496024,0.02348417,0.00162265,0.0002405553,0.01685279,0.0001027354,0.5278245,0.004794474,0.3165252,0.1036412,0.001180956],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01041259,0.003197759,0.9832346,0.0004013444,0.001979902,0.0000230993,0.00001062087,0.00001432467,0.0007256934],"genre_scores_gemma":[0.5928198,0.001976449,0.4039279,0.0001973897,0.0007097401,6.926988e-7,0.000001545934,0.00001840009,0.0003481671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.819022,"threshold_uncertainty_score":0.4022141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04392963441363484,"score_gpt":0.2813158353363322,"score_spread":0.2373862009226974,"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."}}