{"id":"W2070468815","doi":"10.1016/j.jairtraman.2011.10.004","title":"Logical analysis of data for estimating passenger show rates at Air Canada","year":2011,"lang":"en","type":"article","venue":"Journal of Air Transport Management","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Air Canada; Polytechnique Montréal","funders":"","keywords":"Logical analysis; Operations research; Population; Computer science; Statistics; Econometrics; Engineering; Mathematics; Mathematical statistics; Demography","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":[0.001235352,0.0005440693,0.0003751612,0.003693411,0.000801136,0.001313877,0.0007767499,0.000599621,0.001457941],"category_scores_gemma":[0.007010289,0.0001988118,0.0006449859,0.003566165,0.0004492966,0.0004659338,0.0004313716,0.0006094002,0.0004499155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003838585,"about_ca_system_score_gemma":0.006016474,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.665197,"about_ca_topic_score_gemma":0.7210363,"domain_scores_codex":[0.9990368,0.0001902525,0.00007529726,0.0001248228,0.0003927272,0.00018009],"domain_scores_gemma":[0.9942631,0.002844661,0.0004347119,0.0003427503,0.001915972,0.0001987821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008056509,0.0003322109,0.8501507,0.0001436081,0.0002867168,0.0003555404,0.0002862588,0.06696752,0.005757266,0.001540439,0.004777361,0.06859662],"study_design_scores_gemma":[0.0000355772,0.0001540573,0.7253127,0.00002766007,0.0001053563,0.0001432987,0.0008328368,0.2643418,0.004821179,0.0008028857,0.003380636,0.00004200682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824185,0.0002218242,0.005161329,0.0001930872,0.00001317645,0.00005416294,0.01024913,0.0002631476,0.001425589],"genre_scores_gemma":[0.9835249,0.0001083527,0.004185188,0.00001775849,0.000009565281,0.00003014165,0.01135973,0.00001796239,0.000746269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.334803,"threshold_uncertainty_score":0.6735494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04366106932765179,"score_gpt":0.2780978618753474,"score_spread":0.2344367925476956,"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."}}