{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002853728,0.0006321706,0.0008359999,0.001075073,0.0004662352,0.0007844128,0.001942203,0.0009278441,0.001781945],"category_scores_gemma":[0.01498748,0.000647892,0.0009246036,0.0009103696,0.0008877635,0.001493746,0.001670276,0.001979903,0.0005435448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007488988,"about_ca_system_score_gemma":0.001323508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004932047,"about_ca_topic_score_gemma":0.005008065,"domain_scores_codex":[0.9989349,0.0003940455,0.00006174764,0.0002197209,0.0003396668,0.00004983399],"domain_scores_gemma":[0.9948584,0.003460574,0.0003440938,0.0005691068,0.0006476429,0.0001202865],"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":[0.0001982327,0.0001264751,0.004825165,0.0001907638,0.0001214484,0.0002097752,0.000290069,0.7472936,0.006501809,0.03349939,0.001630368,0.2051129],"study_design_scores_gemma":[0.00001021042,0.00001764598,0.0003474496,0.000009829001,0.000005035386,0.00005212683,0.00001540872,0.991294,0.001346116,0.005876471,0.001009591,0.00001611482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002945703,0.000032612,0.9966252,0.00004444924,0.000009410392,0.00001839184,0.00003701327,0.0001398866,0.0001472786],"genre_scores_gemma":[0.1829664,0.0001617159,0.8137171,0.000148784,0.00005871915,0.0002306619,0.000738657,0.0001550837,0.001822811],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004932047,"threshold_uncertainty_score":0.01509207,"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."}}