{"id":"W4283745665","doi":"10.1177/03611981221104802","title":"Assessing and Comparing Data Imputation Techniques for Item Nonresponse in Household Travel Surveys","year":2022,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Missing data; Computer science; Survey data collection; Discriminative model; Data mining; Econometrics; Statistics; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08500925,0.0009095026,0.0009811611,0.002164855,0.001016447,0.001967393,0.002504166,0.001999462,0.00130136],"category_scores_gemma":[0.2618384,0.0006620204,0.002403854,0.003383229,0.001589231,0.003611161,0.002620118,0.002731359,0.0005790457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001432622,"about_ca_system_score_gemma":0.002472556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003514937,"about_ca_topic_score_gemma":0.003861424,"domain_scores_codex":[0.9179967,0.06865722,0.002974275,0.003236111,0.006298837,0.0008369318],"domain_scores_gemma":[0.7430336,0.2132351,0.009433988,0.02157452,0.01191134,0.0008113912],"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":[0.002238195,0.001100926,0.1566392,0.001964785,0.002932142,0.0002644529,0.006184184,0.2237522,0.002484913,0.02581174,0.008373594,0.5682536],"study_design_scores_gemma":[0.0005478006,0.002643421,0.07262892,0.001700175,0.0007734143,0.0008202443,0.005494964,0.8442339,0.01184669,0.03863262,0.02029472,0.0003831073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3195707,0.001843426,0.6666085,0.002998631,0.0003141299,0.001340352,0.0009368631,0.001519431,0.004867948],"genre_scores_gemma":[0.6204491,0.0008506695,0.3747771,0.0006424948,0.00009161652,0.0009531836,0.001141234,0.0001770028,0.0009176364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08500925,"threshold_uncertainty_score":0.4495773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3368722194823058,"score_gpt":0.485810628575043,"score_spread":0.1489384090927372,"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."}}