{"id":"W2941926664","doi":"10.1287/trsc.2019.0969","title":"The Commute Trip-Sharing Problem","year":2020,"lang":"en","type":"preprint","venue":"Transportation Science","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRIPS architecture; Pooling; Vehicle routing problem; Computer science; Routing (electronic design automation); Duration (music); Locality; Generalization; Operations research; Set (abstract data type); Transport engineering; Mathematical optimization; Engineering; Computer network; Mathematics; Artificial intelligence","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":[0.0005155371,0.0002140442,0.0001821696,0.0001228436,0.0004573146,0.000257066,0.001003824,0.00009396342,0.00002865485],"category_scores_gemma":[0.00001637,0.0001847069,0.0000886889,0.001022735,0.0003747124,0.0002512669,0.00001409926,0.0006163709,0.0000317833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007622704,"about_ca_system_score_gemma":0.0002191948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003798519,"about_ca_topic_score_gemma":0.0004915633,"domain_scores_codex":[0.9981136,0.00000705733,0.0006088696,0.0004124206,0.000551823,0.0003062863],"domain_scores_gemma":[0.998972,0.00004649494,0.00009720193,0.0005184893,0.0002388561,0.0001269733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004122824,0.00008196749,0.009122075,0.001070053,0.0001773938,0.00001893197,0.02250288,0.6378622,0.02355963,0.2798022,0.0025513,0.02321017],"study_design_scores_gemma":[0.001313424,0.00006500458,0.6924321,0.0003888247,0.0002513569,0.000002674863,0.001615246,0.1424054,0.01441934,0.05809368,0.08702008,0.001992829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7163856,0.0007236837,0.2377405,0.008951013,0.004778242,0.003545049,0.0008432349,0.004582203,0.02245045],"genre_scores_gemma":[0.9963658,0.0001476089,0.002833973,0.0001284784,0.00004377412,0.0001591088,0.0002153793,0.00002664793,0.00007919747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.68331,"threshold_uncertainty_score":0.7532127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03950920533372262,"score_gpt":0.280293889707339,"score_spread":0.2407846843736164,"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."}}