{"id":"W4389284528","doi":"10.1016/j.jocm.2023.100454","title":"Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices","year":2023,"lang":"en","type":"article","venue":"Journal of Choice Modelling","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ordinal regression; Ordinal data; Computer science; Residual; Logit; Consistency (knowledge bases); Artificial intelligence; Artificial neural network; Ordered logit; Statistics; Econometrics; Machine learning; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.001051654,0.0001371188,0.0004121716,0.0002455399,0.000127194,0.00009475598,0.0002517453,0.0001001754,0.000107259],"category_scores_gemma":[0.0000964375,0.0001476445,0.0001938052,0.0001550889,0.00002903595,0.0005774091,0.00004553267,0.0002092687,0.00005916355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000117734,"about_ca_system_score_gemma":0.00001007258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004779011,"about_ca_topic_score_gemma":0.00001450958,"domain_scores_codex":[0.9984916,0.00001486116,0.0009204749,0.0002166964,0.00004724584,0.000309108],"domain_scores_gemma":[0.9986914,0.0002392078,0.0008031006,0.0001473922,0.00003496054,0.00008387288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003987819,0.00002196299,0.0198628,0.00001553986,0.00005940528,0.000001385174,0.0001343981,0.9775515,0.000008398451,0.0007883637,0.0006486116,0.0008678041],"study_design_scores_gemma":[0.000726111,0.0001211482,0.003929094,0.0000211246,0.00001969273,0.000006079439,0.00007837533,0.9811949,0.00001542552,0.008378748,0.005353051,0.0001562107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4667483,0.001530263,0.5297533,0.0003957493,0.0007034751,0.0001252598,0.00001301991,0.00001806521,0.0007125962],"genre_scores_gemma":[0.9952916,0.0005302975,0.002504188,0.0001757452,0.0007543809,0.00001073003,0.00001469144,0.00003292419,0.0006854755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5285433,"threshold_uncertainty_score":0.6020769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.14154998753376,"score_gpt":0.2570540448490615,"score_spread":0.1155040573153015,"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."}}