{"id":"W4249757114","doi":"10.1177/0361198105192600105","title":"Mixed Logit Model of Activity-Scheduling Time Horizon Incorporating Spatial–Temporal Flexibility Variables","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Time horizon; TRIPS architecture; Scheduling (production processes); Interdependence; Econometrics; Computer science; Schedule; Travel behavior; Operations research; Flexibility (engineering); Discrete choice; Mixed logit; Logit; Logistic regression; Variety (cybernetics); Economics; Operations management; Statistics; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Microeconomics","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":[0.008304491,0.0002247754,0.0006543316,0.0007071056,0.0003793023,0.00007438072,0.0007405319,0.0002194272,0.0005725122],"category_scores_gemma":[0.0002057671,0.0002119328,0.0003792997,0.0007255139,0.0004926274,0.001046833,0.0000140108,0.001298702,0.00009781097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828756,"about_ca_system_score_gemma":0.0002962988,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008424249,"about_ca_topic_score_gemma":0.01026955,"domain_scores_codex":[0.9956824,0.0004226989,0.002063222,0.0004780951,0.0007773023,0.0005762632],"domain_scores_gemma":[0.9968684,0.000460143,0.001346524,0.0004751382,0.0006192744,0.0002305485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001078169,0.0007473973,0.6824497,0.0002689249,0.0002282345,0.000006615026,0.001800792,0.2659659,0.01310599,0.01833337,0.000649278,0.01536566],"study_design_scores_gemma":[0.001999042,0.0007443058,0.8096685,0.0001817661,0.00003504473,2.822706e-7,0.000503113,0.1386858,0.007619444,0.03945801,0.0007656897,0.0003390285],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9608186,0.0001646876,0.03584309,0.001654949,0.0002007094,0.0006111383,0.0001990249,0.00001528956,0.0004924568],"genre_scores_gemma":[0.9839042,0.0002485216,0.01504292,0.00001931428,0.0001671693,0.00003192993,0.00003046578,0.0000450192,0.0005104041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1272801,"threshold_uncertainty_score":0.9981787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2673834063631905,"score_gpt":0.3317180161183925,"score_spread":0.06433460975520205,"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."}}