{"id":"W3131441060","doi":"10.1287/trsc.2020.1028","title":"A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Truncation (statistics); Sample (material); Computer science; Trip distribution; Mathematical optimization; Process (computing); Sampling (signal processing); Maximum likelihood; Algorithm; Data mining; Statistics; Mathematics; Machine learning","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":[0.001081699,0.0001036367,0.0001458776,0.00008182506,0.0009501492,0.0002155781,0.0004114103,0.00005496603,0.0000229988],"category_scores_gemma":[0.0002970935,0.0001098598,0.00002114142,0.001376085,0.0001290196,0.001357967,0.00000547903,0.00007025523,0.000001714688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008462636,"about_ca_system_score_gemma":0.0005755303,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00470933,"about_ca_topic_score_gemma":0.03881563,"domain_scores_codex":[0.9981635,0.00004895495,0.0003092469,0.0007412646,0.0004564183,0.0002806113],"domain_scores_gemma":[0.9984592,0.0002252149,0.000153932,0.0003579221,0.0005988074,0.0002049639],"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":[0.0003273834,0.0001522617,0.3692489,0.00005540515,0.00005335565,0.000007636371,0.08659639,0.4365343,0.01997594,0.04202133,0.00002809656,0.04499893],"study_design_scores_gemma":[0.001702762,0.0001355607,0.3716175,0.0001850555,0.0002403784,0.000002318716,0.02477839,0.5828296,0.007103215,0.003709425,0.006865776,0.0008299639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3551734,0.000004509007,0.6430251,0.0001874568,0.00005493168,0.0002823484,0.001141532,0.00009483038,0.00003584683],"genre_scores_gemma":[0.6612817,0.000003358765,0.3320751,0.00005925353,0.00004364365,0.00005485024,0.006465001,0.000007749579,0.000009331903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.31095,"threshold_uncertainty_score":0.9787235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06315858534908499,"score_gpt":0.3767507232281914,"score_spread":0.3135921378791064,"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."}}