{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.04759232,0.0001445636,0.0003810568,0.001259847,0.002314863,0.0003323726,0.0013251,0.0001033801,0.0001405205],"category_scores_gemma":[0.0009353209,0.0001344141,0.0001622748,0.002247597,0.0006681071,0.0009863779,0.00002062906,0.00160441,7.341038e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004660665,"about_ca_system_score_gemma":0.001302765,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0847875,"about_ca_topic_score_gemma":0.3958668,"domain_scores_codex":[0.9865415,0.008222126,0.001157506,0.0004669906,0.002953963,0.0006579428],"domain_scores_gemma":[0.9939939,0.003535739,0.0003877171,0.0004435702,0.001414729,0.0002242995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001521296,0.0007794363,0.8729332,0.0003417952,0.000156723,0.00006547053,0.03144168,0.005874804,0.001513725,0.004736252,0.002437886,0.07819773],"study_design_scores_gemma":[0.0009071583,0.0002354025,0.9564243,0.0001176373,0.00004132991,1.910756e-7,0.02987957,0.002912358,0.0001418366,0.002510441,0.006672587,0.0001571679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782947,0.0001655363,0.01536706,0.004559023,0.0001452113,0.001223267,0.0001412905,0.00002359892,0.00008036618],"genre_scores_gemma":[0.997662,0.0002949332,0.001401053,0.00003566052,0.0001164654,0.0001565501,0.0001020895,0.00002950098,0.0002017564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3110792,"threshold_uncertainty_score":0.998984,"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."}}