{"id":"W6987759009","doi":"","title":"TWO INCOMPATIBLE OBJECTIVES WITH INDIVIDUAL RESERVE
\\nMODELS: AN APPROACH WITH MULTIVARIATE ADAPTATIVE
\\nREGRESSION SPLINE MODELS","year":2021,"lang":"en","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Spline (mechanical); Smoothing spline; Context (archaeology); Measure (data warehouse); Class (philosophy)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003816117,0.0005348265,0.001088924,0.0006865073,0.0002020585,0.00004270805,0.0008172123,0.0002670219,0.0001537267],"category_scores_gemma":[0.00001748034,0.0004662595,0.0001338708,0.0007344093,0.0003458609,0.0003453381,0.0003032789,0.000581489,0.000002838157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007987339,"about_ca_system_score_gemma":0.0002984267,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.623519,"about_ca_topic_score_gemma":0.9557231,"domain_scores_codex":[0.997233,0.000455496,0.0003262342,0.0008876528,0.0006606319,0.0004369583],"domain_scores_gemma":[0.9975769,0.0003434079,0.0007863464,0.0009542728,0.0001456262,0.0001934735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01561403,0.02078205,0.007663563,0.00747483,0.006346935,0.001183785,0.4731824,0.1744374,0.00027861,0.1895989,0.01560207,0.0878354],"study_design_scores_gemma":[0.03963421,0.001110581,0.03155129,0.007443287,0.001723949,0.0000633839,0.2156068,0.4477403,0.00004373725,0.2511857,0.0003124389,0.003584414],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5265673,0.0006966862,0.2101074,0.0001121918,0.00003404966,0.002822234,0.0006652686,0.0002998611,0.258695],"genre_scores_gemma":[0.5723342,0.0002537169,0.3433852,0.00001542884,0.0001114514,0.00003013226,0.001029614,0.0004492887,0.08239099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3322041,"threshold_uncertainty_score":0.9997789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05196784457482801,"score_gpt":0.2783356685697769,"score_spread":0.2263678239949489,"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."}}